commit 33d0fc31c7160aac5c8ed91e73f1757601ab927e Author: Rob Wiederstein Date: Sat Feb 21 21:19:09 2026 -0500 Initial commit: BAF Lakehouse fraud detection pipeline End-to-end LightGBM fraud detection pipeline built as an R package, orchestrated by targets with data stored in MinIO via Apache Arrow. Includes 6-layer Lakehouse architecture, class imbalance tournament, formally tuned hyperparameters (PR-AUC 0.198), and Quarto RevealJS slides. Co-Authored-By: Claude Sonnet 4.6 diff --git a/.Rbuildignore b/.Rbuildignore new file mode 100644 index 0000000..6319c7e --- /dev/null +++ b/.Rbuildignore @@ -0,0 +1,33 @@ +^\.Rproj\.user$ +^.*\.Rproj$ +^\.Rhistory$ +^\.RData$ +^\.Renviron$ +^\.Rprofile$ +^Rprofile$ + +^renv$ +^renv\.lock$ + +^\.git$ +^\.github$ + +^_targets$ +^_targets\.R$ + +^reports$ + +^index\.qmd$ +^_quarto\.yml$ +^\.quarto$ + +^README\.Rmd$ + +^LICENSE\.md$ +^scratch$ +^TODO\.md$ +^dev$ + +^_pkgdown\.yml$ +^docs$ +^pkgdown$ diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..4a5e932 --- /dev/null +++ b/.dockerignore @@ -0,0 +1 @@ +/dev/ diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6331d08 --- /dev/null +++ b/.gitignore @@ -0,0 +1,32 @@ +# --- RStudio & System Basics --- +.Rproj.user +.Rhistory +.RData +.Ruserdata +.DS_Store + +# --- SECURITY: Never commit these --- +.Renviron +.Rprofile +*.secret +credentials/ + +# --- DATA: The "Lake" Rule --- +# Since data lives on Smaug/MinIO, we ignore any local data attempts +data/* +!data/README.md +*.csv +*.parquet +*.rds + +# --- WORKFLOW: Targets (for when you switch later) --- +_targets/ +_targets.user/data/ +.Renviron + +/.quarto/ +**/*.quarto_ipynb +docs + +/dev/ +/scratch/ diff --git a/DESCRIPTION b/DESCRIPTION new file mode 100644 index 0000000..b7faa04 --- /dev/null +++ b/DESCRIPTION @@ -0,0 +1,34 @@ +Package: baflakehouse +Title: Lakehouse Workflow for the Bank Account Fraud Dataset +Version: 0.0.0.9000 +Authors@R: + person("Rob", "Wiederstein", role = c("aut", "cre"), + email = "REPLACE_ME@example.com") +Description: Tools to ingest the Bank Account Fraud (BAF) Base dataset into a + MinIO/S3-backed lakehouse, clean encoded missing values, and produce + reproducible reporting artifacts (tables, figures, slides) orchestrated with + targets. +License: MIT + file LICENSE +Encoding: UTF-8 +Roxygen: list(markdown = TRUE) +RoxygenNote: 7.3.3 +Imports: + arrow, + colorspace, + cowplot, + dplyr, + tidyr, + stringr, + readr, + gt, + quarto, + ggplot2, + bonsai +Suggests: + duckdb, + targets, + tarchetypes, + knitr, + scales +URL: https://docs.robwiederstein.org/baflakehouse +BugReports: https://git.robwiederstein.org/rkw/bank-fraud-baf-lakehouse/issues diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8cd4469 --- /dev/null +++ b/LICENSE @@ -0,0 +1,2 @@ +YEAR: 2026 +COPYRIGHT HOLDER: Rob Wiederstein diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..8bcf4a9 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,21 @@ +# MIT License + +Copyright (c) 2026 Rob Wiederstein + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/NAMESPACE b/NAMESPACE new file mode 100644 index 0000000..0a0dde4 --- /dev/null +++ b/NAMESPACE @@ -0,0 +1,139 @@ +# Generated by roxygen2: do not edit by hand + +export(build_baf_recipe) +export(clean_baf_base) +export(compute_fraud_by_month) +export(connect_baf) +export(convert_to_parquet) +export(engineer_features) +export(evaluate_final_model) +export(format_class_imbalance_tourney_gt) +export(format_fraud_by_month_gt) +export(generate_model_inputs) +export(plot_conf_mat_heatmap) +export(plot_fraud_by_month) +export(plot_hexbin_interaction) +export(plot_missingness) +export(plot_num_cor) +export(plot_var_imp) +export(prepare_eda_recipe) +export(render_slides) +export(run_imbalance_tournament) +export(save_report_figure) +export(save_report_table) +export(train_diag_model) +export(train_production_model) +importFrom(arrow,S3FileSystem) +importFrom(arrow,open_dataset) +importFrom(arrow,read_csv_arrow) +importFrom(arrow,s3_bucket) +importFrom(arrow,to_duckdb) +importFrom(arrow,write_dataset) +importFrom(arrow,write_parquet) +importFrom(colorspace,qualitative_hcl) +importFrom(colorspace,scale_color_discrete_qualitative) +importFrom(colorspace,scale_fill_continuous_diverging) +importFrom(colorspace,scale_fill_continuous_sequential) +importFrom(corrr,correlate) +importFrom(corrr,rearrange) +importFrom(corrr,shave) +importFrom(corrr,stretch) +importFrom(cowplot,background_grid) +importFrom(cowplot,theme_cowplot) +importFrom(cowplot,theme_half_open) +importFrom(cowplot,theme_minimal_grid) +importFrom(cowplot,theme_minimal_vgrid) +importFrom(dplyr,`%>%`) +importFrom(dplyr,across) +importFrom(dplyr,any_of) +importFrom(dplyr,arrange) +importFrom(dplyr,bind_rows) +importFrom(dplyr,case_when) +importFrom(dplyr,collect) +importFrom(dplyr,desc) +importFrom(dplyr,everything) +importFrom(dplyr,filter) +importFrom(dplyr,group_by) +importFrom(dplyr,if_else) +importFrom(dplyr,mutate) +importFrom(dplyr,n) +importFrom(dplyr,pull) +importFrom(dplyr,rename) +importFrom(dplyr,select) +importFrom(dplyr,slice_max) +importFrom(dplyr,slice_sample) +importFrom(dplyr,summarise) +importFrom(dplyr,summarize) +importFrom(dplyr,tbl_vars) +importFrom(dplyr,ungroup) +importFrom(ggplot2,aes) +importFrom(ggplot2,autoplot) +importFrom(ggplot2,coord_flip) +importFrom(ggplot2,element_blank) +importFrom(ggplot2,element_text) +importFrom(ggplot2,expansion) +importFrom(ggplot2,geom_line) +importFrom(ggplot2,geom_linerange) +importFrom(ggplot2,geom_point) +importFrom(ggplot2,geom_segment) +importFrom(ggplot2,geom_text) +importFrom(ggplot2,geom_tile) +importFrom(ggplot2,ggplot) +importFrom(ggplot2,ggsave) +importFrom(ggplot2,labs) +importFrom(ggplot2,position_dodge) +importFrom(ggplot2,scale_color_manual) +importFrom(ggplot2,scale_fill_gradient) +importFrom(ggplot2,scale_y_continuous) +importFrom(ggplot2,scale_y_log10) +importFrom(ggplot2,stat_summary_hex) +importFrom(ggplot2,theme) +importFrom(ggplot2,theme_minimal) +importFrom(ggrepel,geom_text_repel) +importFrom(glue,glue) +importFrom(gt,cols_label) +importFrom(gt,data_color) +importFrom(gt,fmt_number) +importFrom(gt,gt) +importFrom(gt,tab_header) +importFrom(gt,tab_options) +importFrom(lightgbm,lgb.Dataset) +importFrom(lightgbm,lgb.importance) +importFrom(lightgbm,lgb.save) +importFrom(lightgbm,lgb.train) +importFrom(lubridate,"%m+%") +importFrom(parsnip,boost_tree) +importFrom(parsnip,set_engine) +importFrom(parsnip,set_mode) +importFrom(quarto,quarto_render) +importFrom(readr,write_rds) +importFrom(recipes,all_nominal_predictors) +importFrom(recipes,all_numeric_predictors) +importFrom(recipes,all_predictors) +importFrom(recipes,bake) +importFrom(recipes,prep) +importFrom(recipes,recipe) +importFrom(recipes,step_dummy) +importFrom(recipes,step_impute_median) +importFrom(recipes,step_indicate_na) +importFrom(recipes,step_novel) +importFrom(recipes,step_unknown) +importFrom(recipes,step_zv) +importFrom(recipes,update_role) +importFrom(scales,percent) +importFrom(stats,reorder) +importFrom(stats,sd) +importFrom(stats,t.test) +importFrom(stringr,str_remove) +importFrom(stringr,str_replace_all) +importFrom(themis,adasyn) +importFrom(themis,smote) +importFrom(themis,step_tomek) +importFrom(tidyr,pivot_longer) +importFrom(tidyselect,where) +importFrom(workflows,add_model) +importFrom(workflows,add_recipe) +importFrom(workflows,extract_fit_engine) +importFrom(workflows,fit) +importFrom(workflows,workflow) +importFrom(yardstick,pr_auc) diff --git a/R/baflakehouse-package.R b/R/baflakehouse-package.R new file mode 100644 index 0000000..f5bb9f6 --- /dev/null +++ b/R/baflakehouse-package.R @@ -0,0 +1,9 @@ +#' baflakehouse: Lakehouse Workflow for the Bank Account Fraud Dataset +#' +#' Tools to ingest the Bank Account Fraud (BAF) Base dataset into a MinIO/S3-backed +#' lakehouse, clean encoded missing values, and produce reproducible reporting +#' artifacts orchestrated with targets. +#' +#' @docType _PACKAGE +#' @name baflakehouse-package +NULL diff --git a/R/functions.R b/R/functions.R new file mode 100644 index 0000000..8c4176c --- /dev/null +++ b/R/functions.R @@ -0,0 +1,1210 @@ +#' Convert BAF CSV to partitioned Parquet in MinIO (S3) +#' +#' Reads `Base.csv` from a MinIO/S3 bucket prefix (e.g., `"01_raw"`) and writes a +#' Hive-style partitioned Parquet dataset to another prefix (e.g., `"02_intermediate"`), +#' partitioned by `variant` (e.g., `variant=Base/part-*.parquet`). +#' +#' Connection settings are taken from environment variables: +#' \itemize{ +#' \item \code{BAF_ENDPOINT} (e.g. \code{"minio:9000"} or \code{"192.168.4.xx:9000"}) +#' \item \code{BAF_KEY} (MinIO access key) +#' \item \code{BAF_SECRET} (MinIO secret key) +#' } +#' +#' @param from_prefix Character. Prefix/key under the bucket containing CSVs (e.g. \code{"01_raw"}). +#' @param to_prefix Character. Prefix/key under the bucket to write Parquet dataset (e.g. \code{"02_intermediate"}). +#' @param bucket_name Character. Bucket name. Default \code{"baf-fraud"}. +#' +#' @return A character string giving the destination dataset prefix (typically \code{to_prefix}). +#' +#' @export +#' +#' @importFrom arrow s3_bucket read_csv_arrow write_dataset +#' @importFrom dplyr mutate +#' @importFrom stringr str_remove str_replace_all +#' +#' @examples +#' \dontrun{ +#' Sys.setenv( +#' BAF_ENDPOINT = "minio:9000", +#' BAF_KEY = "YOUR_ACCESS_KEY", +#' BAF_SECRET = "YOUR_SECRET_KEY" +#' ) +#' convert_to_parquet(from_prefix = "01_raw", to_prefix = "02_intermediate", bucket_name = "baf-fraud") +#' } +convert_to_parquet <- function( + from_prefix, + to_prefix, + bucket_name = "baf-fraud" +) { + endpoint <- Sys.getenv("BAF_ENDPOINT") + access_key <- Sys.getenv("BAF_KEY") + secret_key <- Sys.getenv("BAF_SECRET") + + if (endpoint == "") stop("Missing env var: BAF_ENDPOINT") + if (access_key == "") stop("Missing env var: BAF_KEY") + if (secret_key == "") stop("Missing env var: BAF_SECRET") + + bucket <- s3_bucket( + bucket_name, + endpoint_override = endpoint, + scheme = "http", + access_key = access_key, + secret_key = secret_key, + region = "us-east-1" + ) + + path_raw <- bucket$path(from_prefix) + path_out <- bucket$path(to_prefix) + + # List CSVs (Arrow may return full keys; basename() normalizes to file name) + file_list <- basename(path_raw$ls()) + file_list <- file_list[grepl("\\.csv$", file_list, ignore.case = TRUE)] + + # Current mode: only Base.csv (since you've trimmed the bucket) + file_list <- file_list[tolower(file_list) == "base.csv"] + + if (length(file_list) == 0) { + stop("No Base.csv found under ", bucket_name, "/", from_prefix, "/") + } + + message("Found ", length(file_list), " file(s) to process.") + + for (file_name in file_list) { + variant_name <- file_name |> + str_remove("\\.csv$") |> + str_replace_all(" ", "_") # e.g., "Variant I.csv" -> "Variant_I" + + message("\u2714 Processing: ", variant_name, "...") + + df <- read_csv_arrow(path_raw$path(file_name)) |> + mutate(variant = variant_name) + + write_dataset( + df, + path = path_out, + format = "parquet", + partitioning = "variant" + ) + } + message("\u2714 Converted Base.csv to Parquet on MinIO at s3://", bucket_name, "/", to_prefix, "/variant=Base/") + out_base <- bucket$path(to_prefix)$path("variant=Base") + n_parquet <- sum(grepl("\\.parquet$", out_base$ls(), ignore.case = TRUE)) + message("\u2714 Wrote ", n_parquet, " parquet file(s) under variant=Base/") + # Return a stable "artifact pointer" for targets + file.path(to_prefix, "variant=Base") +} + +#' Connect to BAF dataset on MinIO (Arrow or DuckDB) +#' +#' @param prefix Character. Dataset prefix inside the bucket +#' (e.g., "02_intermediate/variant=Base"). +#' @param bucket_name Character. Bucket name. Defaults to env var BAF_BUCKET. +#' @param use_duckdb Logical. If TRUE, return a DuckDB-backed lazy tbl. +#' +#' @return An Arrow Dataset (default) or a DuckDB-backed lazy table. +#' @export +#' +#' @importFrom arrow s3_bucket open_dataset to_duckdb +connect_baf <- function(prefix, bucket_name = Sys.getenv("BAF_BUCKET"), use_duckdb = TRUE) { + + endpoint <- Sys.getenv("BAF_ENDPOINT") + key <- Sys.getenv("BAF_KEY") + secret <- Sys.getenv("BAF_SECRET") + + if (bucket_name == "") stop("Missing env var or arg: BAF_BUCKET / bucket_name") + if (endpoint == "") stop("Missing env var: BAF_ENDPOINT") + if (key == "") stop("Missing env var: BAF_KEY") + if (secret == "") stop("Missing env var: BAF_SECRET") + + b <- arrow::s3_bucket( + bucket_name, + endpoint_override = endpoint, + scheme = "http", + access_key = key, + secret_key = secret, + region = "us-east-1" + ) + + ds <- arrow::open_dataset(b$path(prefix), format = "parquet") + + if (isTRUE(use_duckdb)) { + ds <- arrow::to_duckdb(ds) + message("\u2714 Connected to s3://", bucket_name, "/", prefix, " via DuckDB Engine") + } else { + message("\u2714 Connected to s3://", bucket_name, "/", prefix, " via Arrow Engine") + } + + ds +} + +#' Clean the BAF Base dataset and write to 03_primary +#' +#' @param in_prefix Character. Input dataset prefix inside bucket (e.g. "02_intermediate/variant=Base"). +#' @param out_prefix Character. Output dataset prefix inside bucket (e.g. "03_primary/variant=Base"). +#' @param bucket_name Character. Bucket name. Default "baf-fraud". +#' @param partitioning Character vector of columns to partition by. Default "month". Set NULL to disable. +#' @param existing_data_behavior One of "overwrite", "error", "delete_matching". Default "overwrite". +#' @param verbose Logical. Emit progress messages. Default TRUE. +#' +#' @return Character. out_prefix (for downstream targets). +#' @export +#' +#' @importFrom dplyr mutate if_else select rename tbl_vars +#' @importFrom arrow s3_bucket write_dataset +clean_baf_base <- function( + in_prefix, + out_prefix = "03_primary/variant=Base", + bucket_name = "baf-fraud", + partitioning = "month", + existing_data_behavior = c("overwrite", "error", "delete_matching"), + verbose = TRUE +) { + existing_data_behavior <- match.arg(existing_data_behavior) + + endpoint <- Sys.getenv("BAF_ENDPOINT") + key <- Sys.getenv("BAF_KEY") + secret <- Sys.getenv("BAF_SECRET") + + if (endpoint == "") stop("Missing env var: BAF_ENDPOINT") + if (key == "") stop("Missing env var: BAF_KEY") + if (secret == "") stop("Missing env var: BAF_SECRET") + + if (verbose) message("Beginning cleaning...") + + # Arrow-native dataset (required for Arrow write_dataset) + ds <- connect_baf(in_prefix, bucket_name = bucket_name, use_duckdb = FALSE) + + # 1) outcome label + ds_labeled <- ds |> + mutate(outcome = if_else(fraud_bool == 1L, "Fraud", "Legit")) |> + select(-fraud_bool) + + if (verbose) message("\u2714 Outcome column created as `outcome`") + + # Normalize email column name to match datasheet + vars <- dplyr::tbl_vars(ds_labeled) + if ("device_distinct_emails_8w" %in% vars && !("device_distinct_emails" %in% vars)) { + ds_labeled <- ds_labeled |> rename(device_distinct_emails = device_distinct_emails_8w) + } + + # Re-check after potential rename + vars <- dplyr::tbl_vars(ds_labeled) + required <- c( + "prev_address_months_count", + "current_address_months_count", + "bank_months_count", + "session_length_in_minutes", + "device_distinct_emails", + "intended_balcon_amount" + ) + missing_required <- setdiff(required, vars) + if (length(missing_required) > 0) { + stop("Missing expected columns: ", paste(missing_required, collapse = ", ")) + } + + # 2) sentinel -1 -> NA (Arrow-friendly: explicit if_else per column) + ds_na_recode <- ds_labeled |> + mutate( + prev_address_months_count = if_else(prev_address_months_count == -1L, NA_integer_, prev_address_months_count), + current_address_months_count = if_else(current_address_months_count == -1L, NA_integer_, current_address_months_count), + bank_months_count = if_else(bank_months_count == -1L, NA_integer_, bank_months_count), + session_length_in_minutes = if_else(session_length_in_minutes == -1L, NA_integer_, session_length_in_minutes), + device_distinct_emails = if_else(device_distinct_emails == -1L, NA_integer_, device_distinct_emails) + ) + + if (verbose) message("\u2714 Sentinel (-1) values converted to NA") + + # 3) intended_balcon_amount: negatives are missing -> NA + ds_balcon_recode <- ds_na_recode |> + mutate( + intended_balcon_amount = if_else(intended_balcon_amount < 0, NA_real_, intended_balcon_amount) + ) + + if (verbose) message("\u2714 intended_balcon_amount constrained to values >= 0 (negatives set to NA)") + + # 4) Write to MinIO using arrow s3_bucket path (keeps endpoint_override) + b <- arrow::s3_bucket( + bucket_name, + endpoint_override = endpoint, + scheme = "http", + access_key = key, + secret_key = secret, + region = "us-east-1" + ) + out_path <- b$path(out_prefix) + + if (is.null(partitioning)) { + arrow::write_dataset( + ds_balcon_recode, + path = out_path, + format = "parquet", + existing_data_behavior = existing_data_behavior + ) + } else { + arrow::write_dataset( + ds_balcon_recode, + path = out_path, + format = "parquet", + partitioning = partitioning, + existing_data_behavior = existing_data_behavior + ) + } + + if (verbose) message("\u2714 Wrote cleaned dataset to s3://", bucket_name, "/", out_prefix) + + out_prefix +} +#' Plot applications by month (Legit vs Fraud) on a log scale +#' +#' Builds an exploratory chart of absolute application counts by month +#' split by outcome (Legit vs Fraud). Uses a log10 y-axis so rare fraud +#' remains visible on the same axis. +#' +#' Data source: expects a cleaned "primary" dataset prefix (e.g. 03_primary/variant=Base) +#' stored in MinIO/S3, accessed via \code{connect_baf()}. +#' +#' @param dataset_prefix Character. Prefix inside the bucket, e.g. "03_primary/variant=Base". +#' @param bucket_name Character. Bucket name. Default "baf-fraud". +#' @param palette Character. colorspace qualitative palette name. Default "Dark 3". +#' @param title Character. Plot title. Default "". +#' +#' @return A ggplot object. +#' @export +#' +#' @importFrom dplyr group_by summarise mutate arrange collect rename n +#' @importFrom tidyr pivot_longer +#' @importFrom ggplot2 ggplot aes geom_line geom_point scale_y_log10 labs theme +#' @importFrom cowplot theme_cowplot +#' @importFrom colorspace qualitative_hcl +plot_fraud_by_month <- function( + dataset_prefix, + bucket_name = "baf-fraud", + palette = "Dark 3", + title = "" +) { + ds <- connect_baf(dataset_prefix, bucket_name = bucket_name, use_duckdb = TRUE) + + ds_fraud <- ds |> + dplyr::group_by(month) |> + dplyr::summarise( + Fraud = sum(outcome == "Fraud", na.rm = TRUE), + Legit = sum(outcome == "Legit", na.rm = TRUE), + Total = dplyr::n(), + .groups = "drop" + ) |> + dplyr::mutate(Pct_Fraud = 100 * Fraud / Total) |> + dplyr::arrange(month) |> + dplyr::collect() |> + dplyr::rename(Month = month) |> + tidyr::pivot_longer(c(Fraud, Legit), names_to = "Outcome", values_to = "n") |> + dplyr::mutate(Outcome = factor(Outcome, levels = c("Legit", "Fraud"))) + + cols <- colorspace::qualitative_hcl(2, palette = palette) + names(cols) <- levels(ds_fraud$Outcome) + + ggplot2::ggplot(ds_fraud, ggplot2::aes(x = factor(Month), y = n, group = Outcome, color = Outcome)) + + ggplot2::geom_line(linewidth = 1) + + ggplot2::geom_point(size = 2) + + ggplot2::scale_y_log10( + breaks = c(1e3, 1e4, 1e5), + labels = c("1k", "10k", "100k") + ) + + ggplot2::labs( + title = title, + x = "Month", + y = "Applications (log10 scale)", + color = "Outcome" + ) + + ggplot2::scale_color_manual(values = cols) + + cowplot::theme_cowplot(font_size = 20) +} + +#' Fraud prevalence by month (counts + percent) +#' +#' Computes monthly counts of Fraud/Legit, totals, and percent fraud. +#' +#' @param in_prefix Character. Dataset prefix inside the bucket, e.g. "03_primary/variant=Base". +#' @param use_duckdb Logical. Use DuckDB for lazy querying. Default TRUE. +#' +#' @return A tibble with Month, Fraud, Legit, Total, Pct_Fraud. +#' @export +#' +#' @importFrom dplyr group_by summarise n mutate arrange rename +#' @importFrom dplyr `%>%` +compute_fraud_by_month <- function(in_prefix, use_duckdb = TRUE) { + ds <- connect_baf(in_prefix, use_duckdb = use_duckdb) + + ds %>% + dplyr::group_by(month) %>% + dplyr::summarise( + Fraud = sum(outcome == "Fraud", na.rm = TRUE), + Legit = sum(outcome == "Legit", na.rm = TRUE), + Total = dplyr::n(), + .groups = "drop" + ) %>% + dplyr::mutate(Pct_Fraud = 100 * Fraud / Total) %>% + dplyr::arrange(month) %>% + dplyr::collect() %>% + dplyr::rename(Month = month) +} +#' Format fraud-by-month table as a gt object +#' +#' @param x Tibble from compute_fraud_by_month(). +#' +#' @return A gt table. +#' @export +#' +#' @importFrom gt gt fmt_number cols_label tab_options +format_fraud_by_month_gt <- function(x) { + gt::gt(x) %>% + gt::cols_label( + Month = "Month", + Fraud = "Fraud", + Legit = "Legit", + Total = "Total", + Pct_Fraud = "% Fraud" + ) %>% + gt::fmt_number(columns = c(Fraud, Legit, Total), decimals = 0) %>% + gt::fmt_number(columns = Pct_Fraud, decimals = 2) %>% + gt::tab_options( + table.font.size = "80%", + data_row.padding = gt::px(2) + ) +} +#' Save a report table artifact +#' +#' @param x Object to save. +#' @param filename Output filename, e.g. "tbl_fraud_by_month.rds". +#' @param out_dir Output directory. Default "reports/tables". +#' +#' @return Character path to saved file. +#' @export +#' +#' @importFrom readr write_rds +save_report_table <- function(x, filename, out_dir = "reports/tables") { + dir.create(out_dir, showWarnings = FALSE, recursive = TRUE) + out_path <- file.path(out_dir, filename) + readr::write_rds(x, out_path) + normalizePath(out_path, winslash = "/", mustWork = FALSE) +} + + + +#' Save a report figure artifact +#' +#' Saves a ggplot object to \code{reports/figures/}. +#' Intended for use in `targets` pipelines as a file-producing target. +#' +#' @param plot A ggplot object. +#' @param filename Character. Output filename, e.g. \code{"fig_fraud_by_month.png"}. +#' @param out_dir Character. Output directory. Default \code{"reports/figures"}. +#' @param width,height,dpi Numeric. Passed to \code{ggplot2::ggsave()}. +#' +#' @return Character. Normalized path to the saved file. +#' @export +#' +#' @importFrom ggplot2 ggsave +save_report_figure <- function( + plot, + filename, + out_dir = "reports/figures", + width = 12, + height = 6.75, + dpi = 300 +) { + dir.create(out_dir, showWarnings = FALSE, recursive = TRUE) + out_path <- file.path(out_dir, filename) + ggplot2::ggsave(out_path, plot = plot, width = width, height = height, dpi = dpi) + normalizePath(out_path, winslash = "/", mustWork = FALSE) +} +#' Render Quarto revealjs slideshow after required assets exist +#' +#' @param qmd Character. Input Quarto file (e.g. "index.qmd"). +#' @param assets Character vector. File paths that must exist before rendering. +#' @param output_dir Character. Output directory for rendered slides. +#' +#' @return Character path to the rendered HTML file. +#' @export +#' +#' @importFrom quarto quarto_render +render_slides <- function(qmd = "index.qmd", assets, output_dir = "reports/slides") { + missing <- assets[!file.exists(assets)] + if (length(missing) > 0) { + stop("Missing report assets:\n", paste(missing, collapse = "\n")) + } + + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + + quarto::quarto_render( + input = qmd, + quiet = FALSE, + quarto_args = c("--output-dir", output_dir) + ) + + file.path(output_dir, sub("\\.qmd$", ".html", basename(qmd))) +} +#' Run Class Imbalance Tournament +#' +#' Trains LightGBM models across different class imbalance strategies +#' (Standard, SMOTE, Adasyn, etc.) using sliding time windows. Evaluates +#' performance using PR-AUC and calculates statistical significance. +#' Includes common-sense hyperparameter defaults to prevent overfitting. +#' +#' @param tasks A tibble containing recipe_name, data_folder, and scale_pos_weight. +#' @param windows A tibble containing window_id, train_months, and test_month. +#' @param feature_prefix Character. The upstream dependency prefix (used to force DAG execution). +#' @param bucket_name Character. Bucket name. Default "baf-fraud". +#' @param inputs_prefix Character. The folder containing the sampled data. Default "05_model_input". +#' +#' @return A tibble with the summarized tournament results. +#' @export +#' +#' @importFrom arrow s3_bucket open_dataset +#' @importFrom dplyr filter collect select any_of bind_rows +#' @importFrom lightgbm lgb.Dataset lgb.train +#' @importFrom yardstick pr_auc +#' @importFrom glue glue +run_imbalance_tournament <- function( + tasks, + windows, + feature_prefix, + bucket_name = "baf-fraud", + inputs_prefix = "05_model_input" +) { + + endpoint <- Sys.getenv("BAF_ENDPOINT") + key <- Sys.getenv("BAF_KEY") + secret <- Sys.getenv("BAF_SECRET") + + if (endpoint == "") stop("Missing env var: BAF_ENDPOINT") + + b <- arrow::s3_bucket( + bucket_name, + endpoint_override = endpoint, + scheme = "http", + access_key = key, + secret_key = secret, + region = "us-east-1" + ) + + results_log <- list() + counter <- 1 + + # 1. THE LOOP + for (i in seq_len(nrow(tasks))) { + task <- tasks[i, ] + + for (j in seq_len(nrow(windows))) { + win <- windows[j, ] + + message(glue::glue("\n\u2699\ufe0f {task$recipe_name} | {win$window_id}")) + + # Load Training Data + train_df <- arrow::open_dataset(b$path(glue::glue("{inputs_prefix}/{task$data_folder}"))) |> + dplyr::filter(month %in% win$train_months[[1]]) |> + dplyr::collect() + + X_train <- train_df |> + dplyr::select(-outcome, -dplyr::any_of(c("month", "month_date"))) |> + as.matrix() + y_train <- as.numeric(train_df$outcome == "Fraud") + + # Train Model (with strict overfitting brakes) + dtrain <- lightgbm::lgb.Dataset(data = X_train, label = y_train) + start_time <- Sys.time() + + model <- lightgbm::lgb.train( + params = list( + objective = "binary", + metric = "auc", + learning_rate = 0.05, + + # --- The Common Sense Defaults --- + max_depth = 6, + num_leaves = 31, + min_data_in_leaf = 250, + feature_fraction = 0.8, + bagging_fraction = 0.8, + bagging_freq = 1, + # --------------------------------- + + device = "cpu", + scale_pos_weight = task$scale_pos_weight + ), + data = dtrain, + nrounds = 500, + verbose = -1 + ) + + end_time <- Sys.time() + runtime <- as.numeric(difftime(end_time, start_time, units = "secs")) + + # Load Testing Data (Always evaluate on the baseline) + test_df <- arrow::open_dataset(b$path(glue::glue("{inputs_prefix}/baseline"))) |> + dplyr::filter(month == win$test_month) |> + dplyr::collect() + + X_test <- test_df |> + dplyr::select(-outcome, -dplyr::any_of(c("month", "month_date"))) |> + as.matrix() + + preds <- predict(model, X_test) + + # Score Model + eval_df <- data.frame( + truth = factor(test_df$outcome, levels = c("Fraud", "Legit")), + prob = preds + ) + + score <- yardstick::pr_auc(eval_df, truth, prob)$.estimate + + message(glue::glue(" -> PR-AUC: {round(score, 4)} | Time: {round(runtime, 2)}s")) + + results_log[[counter]] <- data.frame( + recipe = task$recipe_name, + window = win$window_id, + pr_auc = score, + runtime_sec = runtime + ) + counter <- counter + 1 + + # Cleanup + rm(train_df, X_train, y_train, dtrain, model, test_df, X_test, preds, eval_df) + gc() + } + } + + # Return the raw log for downstream targets to handle + results_df <- dplyr::bind_rows(results_log) + return(results_df) +} + +#' Format Class Imbalance Tournament Table +#' +#' Aggregates results from the model tournament and performs paired t-tests +#' against the 'Standard' model to determine statistical significance. +#' +#' @param results_df The tibble output from `run_imbalance_tournament`. +#' +#' @importFrom dplyr filter arrange pull group_by summarize mutate case_when desc +#' @importFrom gt gt tab_header fmt_number data_color +#' @importFrom stats t.test +#' +#' @return A formatted gt table object. +#' @export +format_class_imbalance_tourney_gt <- function(results_df) { + + # Extract scores for the 'Standard' recipe to use as the baseline for t-tests + standard_scores <- results_df |> + dplyr::filter(recipe == "Standard") |> + dplyr::arrange(window) |> + dplyr::pull(pr_auc) + + # Internal helper to calculate p-values vs the Standard baseline + get_p_value <- function(target_recipe, df) { + if (target_recipe == "Standard") return(1.0) + + target_scores <- df |> + dplyr::filter(recipe == target_recipe) |> + dplyr::arrange(window) |> + dplyr::pull(pr_auc) + + tryCatch({ + # Paired t-test accounts for the same windows/seeds being used + test <- stats::t.test(target_scores, standard_scores, paired = TRUE) + test$p.value + }, error = function(e) NA_real_) + } + + # Aggregating window results into a final summary + final_stats <- results_df |> + dplyr::group_by(recipe) |> + dplyr::summarize( + avg_pr_auc = mean(pr_auc), + avg_runtime = mean(runtime_sec), + p_val_vs_std = get_p_value(unique(recipe), results_df) + ) |> + dplyr::mutate( + significance = dplyr::case_when( + recipe == "Standard" ~ "-", + p_val_vs_std < 0.05 ~ "Yes (*)", + TRUE ~ "No (ns)" + ) + ) |> + dplyr::arrange(dplyr::desc(avg_pr_auc)) + + # Formatting with gt for the Quarto presentation + final_stats |> + gt::gt() |> + gt::tab_header( + title = "Class Imbalance Strategy Showdown", + subtitle = "Paired t-test comparison against 'Standard' baseline" + ) |> + gt::fmt_number(columns = c(avg_pr_auc, p_val_vs_std), decimals = 4) |> + gt::data_color( + columns = avg_pr_auc, + palette = c("#ffcccc", "#ffffff", "#ccffcc") # Red-White-Green scale + ) +} + +#' Create Effectiveness vs Efficiency Plot +#' @param results_df Tibble from run_imbalance_tournament +#' @importFrom ggplot2 ggplot aes geom_point scale_color_manual labs theme_minimal +#' @importFrom ggrepel geom_text_repel +#' @importFrom cowplot theme_half_open background_grid +create_efficiency_plot <- function(results_df) { + # Aggregate by recipe + plot_data <- results_df |> + dplyr::group_by(recipe) |> + dplyr::summarize( + avg_pr_auc = mean(pr_auc), + avg_time = mean(runtime_sec) + ) + + ggplot2::ggplot(plot_data, ggplot2::aes(x = avg_time, y = avg_pr_auc)) + + ggplot2::geom_point(ggplot2::aes(color = recipe == "Standard"), size = 5) + + ggplot2::scale_color_manual(values = c("TRUE" = "#E74C3C", "FALSE" = "#2C3E50")) + + ggrepel::geom_text_repel( + ggplot2::aes(label = recipe, fontface = ifelse(recipe == "Standard", "bold", "plain")), + family = "Atkinson Hyperlegible" + ) + + ggplot2::labs( + title = "Strategy Showdown", + x = "Avg Training Time (s)", + y = "PR-AUC" + ) + + cowplot::theme_half_open(font_family = "Atkinson Hyperlegible") + + cowplot::background_grid(major = "y") +} +#' Prepare EDA Recipe +#' @param eda_data Raw EDA data +#' @importFrom recipes recipe update_role step_novel step_unknown step_impute_median step_dummy all_nominal_predictors all_numeric_predictors prep +#' @export +prepare_eda_recipe <- function(eda_data) { + recipe(outcome ~ ., data = eda_data) |> + update_role(month, new_role = "ID") |> + step_novel(all_nominal_predictors()) |> + step_unknown(all_nominal_predictors()) |> + step_impute_median(all_numeric_predictors()) |> + step_dummy(all_nominal_predictors(), one_hot = TRUE) |> + prep() +} + +#' Train Diagnostic Model +#' @param baked_data Baked EDA data +#' @importFrom dplyr select +#' @importFrom lightgbm lgb.Dataset lgb.train +#' @export +train_diag_model <- function(baked_data) { + X_eda <- select(baked_data, -outcome, -month) + X_eda <- as.matrix(X_eda) + y_eda <- as.numeric(baked_data$outcome == "Fraud") + + dtrain <- lgb.Dataset(data = X_eda, label = y_eda) + + lgb.train( + params = list(objective = "binary", metric = "auc", device = "cpu"), + data = dtrain, + nrounds = 100, + verbose = -1 + ) +} + +#' Plot Variable Importance +#' @param model Trained LightGBM model +#' @param title Character. Plot title. Default "". +#' @importFrom lightgbm lgb.importance +#' @importFrom dplyr slice_max +#' @importFrom ggplot2 ggplot aes geom_segment geom_point coord_flip scale_y_continuous labs expansion +#' @importFrom cowplot theme_minimal_vgrid +#' @importFrom stats reorder +#' @importFrom scales percent +#' @export +plot_var_imp <- function(model, title = "") { + importance_df <- lgb.importance(model, percentage = TRUE) + plot_data <- slice_max(importance_df, Gain, n = 15) + + ggplot(plot_data, aes(x = reorder(Feature, Gain), y = Gain)) + + geom_segment(aes(xend = reorder(Feature, Gain), yend = 0), linewidth = 0.8) + + geom_point(size = 3.5) + + coord_flip() + + scale_y_continuous(labels = percent, expand = expansion(mult = c(0, 0.05))) + + labs( + title = title, + x = NULL, y = "Relative Importance" + ) + + theme_minimal_vgrid(font_family = "Atkinson Hyperlegible") +} + +#' Plot Hexbin Interaction +#' @param baked_data Baked EDA data +#' @param title Character. Plot title. Default "". +#' @importFrom dplyr mutate +#' @importFrom ggplot2 ggplot aes stat_summary_hex labs +#' @importFrom colorspace scale_fill_continuous_sequential +#' @importFrom cowplot theme_minimal_grid +#' @importFrom scales percent +#' @export +plot_hexbin_interaction <- function(baked_data, title = "") { + plot_data <- mutate(baked_data, fraud_flag = ifelse(outcome == "Fraud", 1, 0)) + + ggplot(plot_data, aes(x = current_address_months_count, y = credit_risk_score, z = fraud_flag)) + + stat_summary_hex( + bins = 30, + fun = function(z) if (length(z) >= 50) mean(z) else NA_real_ + ) + + scale_fill_continuous_sequential( + palette = "Viridis", + labels = percent, + na.value = "transparent", + rev = FALSE + ) + + labs( + title = title, + x = "Months at Current Address", y = "Credit Risk Score", fill = "Fraud Rate" + ) + + theme_minimal_grid(font_family = "Atkinson Hyperlegible") +} + +#' Plot Missingness Signal +#' @param eda_data Raw EDA data +#' @param title Character. Plot title. Default "". +#' @importFrom dplyr group_by summarise across everything filter +#' @importFrom tidyr pivot_longer +#' @importFrom ggplot2 ggplot aes geom_linerange geom_point coord_flip scale_y_continuous labs position_dodge theme expansion +#' @importFrom colorspace scale_color_discrete_qualitative +#' @importFrom cowplot theme_minimal_vgrid +#' @importFrom scales percent +#' @importFrom stats reorder +#' @export +plot_missingness <- function(eda_data, title = "") { + missing_summary <- eda_data |> + group_by(outcome) |> + summarise(across(everything(), ~ mean(is.na(.x))), .groups = "drop") |> + pivot_longer(cols = -outcome, names_to = "feature", values_to = "pct_missing") |> + filter(pct_missing > 0.05) + + ggplot(missing_summary, aes(x = reorder(feature, pct_missing), y = pct_missing, color = outcome)) + + geom_linerange( + aes(ymin = 0, ymax = pct_missing), + position = position_dodge(width = 0.5), + linewidth = 0.8 + ) + + geom_point(position = position_dodge(width = 0.5), size = 4) + + coord_flip() + + scale_y_continuous( + labels = percent, + expand = expansion(mult = c(0, 0.1)), + breaks = seq(0, 1, by = 0.25) + ) + + scale_color_discrete_qualitative(palette = "Dark 3") + + labs( + title = title, + x = NULL, y = "Percent Missing", color = "Outcome" + ) + + theme_minimal_vgrid(font_family = "Atkinson Hyperlegible") + + theme(legend.position = "right") +} + +#' Plot Numeric Correlation Matrix +#' @param eda_data Raw EDA data +#' @param title Character. Plot title. Default "". +#' @importFrom dplyr select +#' @importFrom tidyselect where +#' @importFrom stats sd +#' @importFrom corrr correlate rearrange shave stretch +#' @importFrom ggplot2 ggplot aes geom_tile geom_text labs theme element_text element_blank expansion +#' @importFrom colorspace scale_fill_continuous_diverging +#' @importFrom cowplot theme_minimal_vgrid +#' @export +plot_num_cor <- function(eda_data, title = "") { + cor_numeric_only <- eda_data |> + select(where(is.numeric), -month) |> + select(where(~ isTRUE(sd(.x, na.rm = TRUE) > 0))) |> + correlate(quiet = TRUE) |> + rearrange() |> + shave() + + cor_long <- stretch(cor_numeric_only, na.rm = TRUE) + + ggplot(cor_long, aes(x = x, y = y, fill = r)) + + geom_tile(color = "white", linewidth = 0.5) + + scale_fill_continuous_diverging( + palette = "Green-Brown", + mid = 0, + limit = c(-1, 1), + name = "Pearson (r)", + expand = expansion(mult = c(0, 0)) + ) + + geom_text( + aes(label = ifelse(abs(r) > 0.2, round(r, 2), "")), + color = "black", + size = 3.5, + family = "Atkinson Hyperlegible" + ) + + labs( + title = title, + x = NULL, y = NULL + ) + + theme_minimal_vgrid(font_family = "Atkinson Hyperlegible") + + theme( + axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 10, face = "bold"), + axis.text.y = element_text(size = 10, face = "bold"), + panel.grid.major = element_blank() + ) +} + +#' Engineer features for the BAF dataset +#' +#' Reads the primary BAF dataset and engineers new features, such as +#' `n_missing`, which counts the number of missing values across key +#' tenure and financial columns. This calculation is performed out-of-memory +#' using Arrow compute. +#' +#' @param in_prefix Character. Input dataset prefix (e.g., "03_primary/variant=Base"). +#' @param out_prefix Character. Output dataset prefix (e.g., "04_feature/variant=Base"). +#' @param bucket_name Character. The S3/MinIO bucket name. Default "baf-fraud". +#' @param partitioning Character vector. Columns to partition by. Default "month". +#' @param existing_data_behavior Character. Behavior when data exists. Default "delete_matching". +#' @param verbose Logical. Whether to print progress messages. Default TRUE. +#' +#' @return Character. The output prefix path for downstream targets. +#' @export +#' +#' @importFrom arrow s3_bucket open_dataset write_dataset +#' @importFrom dplyr mutate +engineer_features <- function( + in_prefix = "03_primary/variant=Base", + out_prefix = "04_feature/variant=Base", + bucket_name = "baf-fraud", + partitioning = "month", + existing_data_behavior = "delete_matching", + verbose = TRUE +) { + + endpoint <- Sys.getenv("BAF_ENDPOINT") + key <- Sys.getenv("BAF_KEY") + secret <- Sys.getenv("BAF_SECRET") + + if (endpoint == "") stop("Missing env var: BAF_ENDPOINT") + + if (verbose) message("Connecting to MinIO bucket: ", bucket_name) + + b <- arrow::s3_bucket( + bucket_name, + endpoint_override = endpoint, + scheme = "http", + access_key = key, + secret_key = secret, + region = "us-east-1" + ) + + if (verbose) message("Opening primary dataset: ", in_prefix) + ds_primary <- arrow::open_dataset(b$path(in_prefix), format = "parquet") + + if (verbose) message("Engineering 'n_missing' feature...") + ds_feature <- ds_primary |> + dplyr::mutate( + n_missing = as.integer(is.na(prev_address_months_count)) + + as.integer(is.na(current_address_months_count)) + + as.integer(is.na(bank_months_count)) + + as.integer(is.na(session_length_in_minutes)) + + as.integer(is.na(device_distinct_emails)) + + as.integer(is.na(intended_balcon_amount)) + ) + + if (verbose) message("Writing feature dataset to: ", out_prefix) + arrow::write_dataset( + dataset = ds_feature, + path = b$path(out_prefix), + format = "parquet", + partitioning = partitioning, + existing_data_behavior = existing_data_behavior + ) + + if (verbose) message("\u2714 Feature engineering complete!") + + out_prefix +} + +#' Generate Resampled Model Inputs +#' +#' Reads the engineered feature layer, prepares a base tidymodels recipe, +#' and generates resampled datasets (Baseline, Under, SMOTE, Adasyn, Tomek) +#' across all months, saving them to the 05_model_input prefix. +#' +#' @param feature_prefix Character. Input prefix (e.g., "04_feature/variant=Base"). +#' @param out_prefix Character. Output prefix base (e.g., "05_model_input"). +#' @param bucket_name Character. Bucket name. Default "baf-fraud". +#' +#' @return Character. The output prefix (for targets dependency tracking). +#' @export +#' +#' @importFrom arrow s3_bucket open_dataset write_parquet +#' @importFrom dplyr filter collect mutate group_by slice_sample ungroup select +#' @importFrom recipes recipe update_role step_novel step_unknown step_indicate_na step_impute_median step_dummy step_zv all_nominal_predictors all_numeric_predictors all_predictors prep bake +#' @importFrom themis smote adasyn step_tomek +#' @importFrom lubridate %m+% +#' @importFrom glue glue +generate_model_inputs <- function( + feature_prefix = "04_feature/variant=Base", + out_prefix = "05_model_input", + bucket_name = "baf-fraud" +) { + + endpoint <- Sys.getenv("BAF_ENDPOINT") + key <- Sys.getenv("BAF_KEY") + secret <- Sys.getenv("BAF_SECRET") + + if (endpoint == "") stop("Missing env var: BAF_ENDPOINT") + + b <- s3_bucket( + bucket_name, + endpoint_override = endpoint, + scheme = "http", + access_key = key, + secret_key = secret, + region = "us-east-1" + ) + + message("Opening feature dataset: ", feature_prefix) + ds_feature <- open_dataset(b$path(feature_prefix)) + + # 1. Prep Sample with the Date Column + message("Preparing base recipe on Month 0 sample...") + sample_data <- ds_feature |> + filter(month == 0) |> + head(5000) |> + collect() |> + mutate(month_date = as.Date("2025-02-01") %m+% months(month)) + + rec_base <- recipe(outcome ~ ., data = sample_data) |> + update_role(month_date, new_role = "ID") |> + step_novel(all_nominal_predictors()) |> + step_unknown(all_nominal_predictors()) |> + step_indicate_na(all_numeric_predictors()) |> + step_impute_median(all_numeric_predictors()) |> + step_dummy(all_nominal_predictors(), one_hot = TRUE) |> + step_zv(all_predictors()) |> + prep() + + # 2. The S3-to-S3 Loop + for (m in 0:7) { + message("Baking and sampling month ", m, "...") + + raw_df <- ds_feature |> + filter(month == m) |> + collect() |> + mutate(month_date = as.Date("2025-02-01") %m+% months(month)) + + baked_df <- bake(rec_base, new_data = raw_df) + + # SAVE BASELINE + write_parquet(baked_df, b$path(glue("{out_prefix}/baseline/month={m}/part-0.parquet"))) + + # PREP NUMERIC-ONLY FOR SAMPLING + numeric_only_df <- baked_df |> select(-month_date) + + # Fork: Under + baked_under <- numeric_only_df |> group_by(outcome) |> slice_sample(prop = 0.25) |> ungroup() + write_parquet(baked_under, b$path(glue("{out_prefix}/under/month={m}/part-0.parquet"))) + + # Fork: Smote + baked_smote <- smote(numeric_only_df, var = "outcome", over_ratio = 0.5) + write_parquet(baked_smote, b$path(glue("{out_prefix}/smote/month={m}/part-0.parquet"))) + + # Fork: Adasyn + baked_adasyn <- adasyn(numeric_only_df, var = "outcome", over_ratio = 0.5, k = 5) + write_parquet(baked_adasyn, b$path(glue("{out_prefix}/adasyn/month={m}/part-0.parquet"))) + + # Fork: Tomek + baked_tomek <- recipe(outcome ~ ., data = numeric_only_df) |> + step_tomek(outcome) |> + prep() |> + bake(new_data = NULL) + write_parquet(baked_tomek, b$path(glue("{out_prefix}/tomek/month={m}/part-0.parquet"))) + + # Cleanup RAM after each month + rm(raw_df, baked_df, numeric_only_df, baked_under, baked_smote, baked_adasyn, baked_tomek) + gc() + } + + message("\u2714 Model inputs generated successfully!") + out_prefix +} + +#' Final Model Evaluation (Months 6 & 7) +#' +#' Trains the winning strategy on the full training set (Months 0-5) +#' and evaluates it on the unseen test set (Months 6-7). +#' +#' @param params A named list of LightGBM hyperparameters with elements: +#' \code{trees}, \code{tree_depth}, \code{learn_rate}, \code{loss_reduction}, \code{min_n}. +#' @param bucket_name Character. Bucket name. Default "baf-fraud". +#' @param inputs_prefix Character. Model input prefix. Default "05_model_input". +#' +#' @return A tibble with columns \code{truth}, \code{prob}, and \code{pred_class}. +#' @export +evaluate_final_model <- function(params, bucket_name = "baf-fraud", inputs_prefix = "05_model_input") { + + b <- arrow::s3_bucket(bucket_name, endpoint_override = Sys.getenv("BAF_ENDPOINT"), + scheme = "http", access_key = Sys.getenv("BAF_KEY"), + secret_key = Sys.getenv("BAF_SECRET"), region = "us-east-1") + + # 1. FULL TRAIN (Months 0-5) + train_df <- arrow::open_dataset(b$path(glue::glue("{inputs_prefix}/baseline"))) |> + dplyr::filter(month %in% 0:5) |> dplyr::collect() + + X_train <- as.matrix(train_df |> dplyr::select(-outcome, -dplyr::any_of(c("month", "month_date")))) + y_train <- as.numeric(train_df$outcome == "Fraud") + + model <- lightgbm::lgb.train( + params = list( + objective = "binary", + metric = "auc", + learning_rate = params$learn_rate, + max_depth = params$tree_depth, + num_leaves = 2^params$tree_depth - 1L, + min_data_in_leaf = params$min_n + ), + data = lightgbm::lgb.Dataset(X_train, label = y_train), nrounds = params$trees, verbose = -1 + ) + + # 2. FINAL EXAM (Months 6-7) + test_df <- arrow::open_dataset(b$path(glue::glue("{inputs_prefix}/baseline"))) |> + dplyr::filter(month %in% 6:7) |> dplyr::collect() + + X_test <- as.matrix(test_df |> dplyr::select(-outcome, -dplyr::any_of(c("month", "month_date")))) + preds <- predict(model, X_test) + + # 3. GENERATE METRICS + eval_df <- dplyr::tibble( + truth = factor(test_df$outcome, levels = c("Fraud", "Legit")), + prob = preds, + pred_class = factor(ifelse(prob >= 0.05, "Fraud", "Legit"), levels = c("Fraud", "Legit")) + ) + + return(eval_df) +} + +#' Plot Confusion Matrix Heatmap +#' +#' Generates a styled 4-quadrant heatmap from a yardstick confusion matrix. +#' +#' @param cm A yardstick conf_mat object. +#' @param title Character. The main title of the plot. +#' @param subtitle Character. The subtitle of the plot. +#' +#' @return A ggplot object. +#' @export +#' +#' @importFrom ggplot2 autoplot scale_fill_gradient labs theme_minimal theme element_text +plot_conf_mat_heatmap <- function( + cm, + title = "" +) { + + p <- ggplot2::autoplot(cm, type = "heatmap") + + ggplot2::scale_fill_gradient(low = "#F3F4F6", high = "#1D4ED8") + + ggplot2::labs( + title = title + ) + + ggplot2::theme_minimal(base_size = 14) + + ggplot2::theme( + legend.position = "none", + plot.title = ggplot2::element_text(face = "bold") + ) + + return(p) +} +#' Train and Serialize Production LightGBM Model +#' +#' Trains a LightGBM model on the complete dataset using the winning +#' hyperparameters, serializes it to a text file, and uploads it directly +#' to MinIO via the Apache Arrow S3 interface. +#' +#' @param data A data frame containing the full BAF dataset (Months 0-7). +#' @param recipe A prepared tidymodels recipe. +#' @param best_params A list or tibble of the winning hyperparameters. +#' @param model_filename Character. The target filename. Defaults to "lgbm_prod.txt". +#' +#' @return Character. The MinIO URI of the uploaded model artifact. +#' @export +#' +#' @importFrom parsnip boost_tree set_engine set_mode +#' @importFrom workflows workflow add_recipe add_model fit extract_fit_engine +#' @importFrom lightgbm lgb.save +#' @importFrom arrow S3FileSystem +train_production_model <- function(data, recipe, best_params, model_filename = "lgbm_prod.txt") { + + # 1. Define the production model specification + lgbm_spec <- parsnip::boost_tree( + trees = best_params$trees, + tree_depth = best_params$tree_depth, + learn_rate = best_params$learn_rate, + min_n = best_params$min_n + ) |> + parsnip::set_engine("lightgbm", is_unbalance = TRUE) |> + parsnip::set_mode("classification") + + # 2. Bundle the workflow and fit to the ENTIRE dataset + prod_wflow <- workflows::workflow() |> + workflows::add_recipe(recipe) |> + workflows::add_model(lgbm_spec) + + fitted_prod_wflow <- workflows::fit(prod_wflow, data = data) + + # 3. Extract the raw LightGBM C++ booster object + lgbm_booster <- workflows::extract_fit_engine(fitted_prod_wflow) + + # 4. Serialize to local disk temporarily + temp_dir <- tempdir() + local_path <- file.path(temp_dir, model_filename) + lightgbm::lgb.save(lgbm_booster, local_path) + + # 5. Connect to MinIO via Arrow using exact .Renviron credentials + s3 <- arrow::S3FileSystem$create( + access_key = Sys.getenv("BAF_KEY"), + secret_key = Sys.getenv("BAF_SECRET"), + endpoint_override = Sys.getenv("BAF_ENDPOINT"), + scheme = "http" # 172.19.0.1 is an internal IP, using HTTP over port 9100 + ) + + # 6. Open an Arrow output stream and push the binary data to MinIO + bucket_name <- Sys.getenv("BAF_BUCKET") + s3_path <- file.path(bucket_name, "06_models", model_filename) + + out_stream <- s3$OpenOutputStream(s3_path) + file_size <- file.info(local_path)$size + raw_bytes <- readBin(local_path, "raw", n = file_size) + + out_stream$write(raw_bytes) + out_stream$close() + + # Clean up the local temporary file + unlink(local_path) + + # 7. Return the storage URI for pipeline tracking + paste0("minio://", s3_path) +} +#' Build Untrained BAF Recipe +#' +#' @param data A data frame +#' +#' @return An untrained tidymodels recipe +#' @export +#' +#' @importFrom recipes recipe update_role step_novel step_unknown step_indicate_na step_impute_median step_dummy step_zv all_nominal_predictors all_numeric_predictors all_predictors +build_baf_recipe <- function(data) { + recipes::recipe(outcome ~ ., data = data) |> + recipes::update_role(month, new_role = "ID") |> + recipes::step_novel(recipes::all_nominal_predictors()) |> + recipes::step_unknown(recipes::all_nominal_predictors()) |> + recipes::step_indicate_na(recipes::all_numeric_predictors()) |> + recipes::step_impute_median(recipes::all_numeric_predictors()) |> + recipes::step_dummy(recipes::all_nominal_predictors(), one_hot = TRUE) |> + recipes::step_zv(recipes::all_predictors()) + + # Notice: NO prep() here! +} \ No newline at end of file diff --git a/R/zzz.R b/R/zzz.R new file mode 100644 index 0000000..1d9ead9 --- /dev/null +++ b/R/zzz.R @@ -0,0 +1,10 @@ +# fix R CMD check notes for dplyr non-standard evaluation +utils::globalVariables(c( + "fraud_bool", "outcome", "month", + "Fraud", "Legit", "Total", "Pct_Fraud", + "Outcome", "Month", + "prev_address_months_count", "current_address_months_count", + "bank_months_count", "session_length_in_minutes", + "device_distinct_emails_8w", "device_distinct_emails", + "intended_balcon_amount" +)) diff --git a/README.md b/README.md new file mode 100644 index 0000000..43eec68 --- /dev/null +++ b/README.md @@ -0,0 +1,76 @@ +--- +output: github_document +--- + +- [baflakehouse](#baflakehouse) + - [About](#about) + - [Results](#results) + - [Clone](#clone) + - [Acknowledgements](#acknowledgements) + - [Citation](#citation) + +# baflakehouse + +## About + +The baflakehouse package is an end-to-end machine learning pipeline built to detect credit card fraud. Rather than relying on static local files, it implements a modern Lakehouse architecture. It ingests a massive 1-million-row dataset, partitions it into Parquet files via Apache Arrow, stores it on a MinIO object server, and trains a production-ready LightGBM model orchestrated entirely by the targets package. +Significance + +Financial fraud datasets suffer from extreme class imbalance, making traditional accuracy metrics highly misleading. This pipeline is engineered specifically to handle that imbalance without aggressive synthetic oversampling. + +## Pipeline + +The pipeline is orchestrated by the `targets` package and executes as a reproducible DAG. All data is stored remotely in MinIO and accessed via Apache Arrow — no local CSVs or intermediate files on disk. + +**Layer 01 → 02 | Ingest** +Raw CSVs are read from `baf-fraud/01_raw` and converted to Hive-partitioned Parquet files in `02_intermediate` using Arrow's `write_dataset()`. + +**Layer 02 → 03 | Clean** +Sentinel values (`-1`) are recoded to `NA`, the binary outcome is relabelled from `fraud_bool` to `outcome` ("Fraud"/"Legit"), and the cleaned data is written to `03_primary` partitioned by month. + +**Layer 03 → 04 | Feature Engineering** +A missingness count feature (`n_missing`) is computed out-of-memory via Arrow compute and written to `04_feature`. + +**Layer 04 → 05 | Resampling** +Five versions of each monthly slice are generated — Baseline, Undersampling, SMOTE, ADASYN, and Tomek Links — and saved to `05_model_input`. + +**Imbalance Tournament** +LightGBM models are trained across all five strategies using three sliding time windows (train on months t, t+1, t+2; test on t+3). Strategies are ranked by PR-AUC and evaluated for statistical significance via paired t-test against the Standard baseline. + +| Strategy | PR-AUC | Avg Train Time (s) | Sig. vs Standard | +|---|---|---|---| +| Standard | 0.1650 | 2.19 | — | +| ADASYN | 0.1629 | 3.87 | No (p = 0.37) | +| SMOTE | 0.1617 | 3.79 | No (p = 0.15) | +| Weighted | 0.1577 | 2.18 | No (p = 0.15) | +| Tomek | 0.1483 | 2.16 | **Yes (p = 0.009)** | +| Undersampling | 0.1394 | 0.92 | **Yes (p = 0.029)** | + +The Standard baseline wins outright. SMOTE and ADASYN offer no statistically significant gain while nearly doubling training time. Tomek Links and Undersampling significantly *hurt* performance and are discarded. + +**Layer 05 → 06 | Production** +The winning Standard strategy is retrained on months 0–5, evaluated on the held-out months 6–7, serialised to `baf_lgbm_prod_v1.txt`, and uploaded to `baf-fraud/06_models` in MinIO. + +**Reporting** +All figures and tables are written to `reports/` and assembled into a Quarto RevealJS slide deck via `tar_quarto()`. + +## Results + +By leveraging LightGBM's native cost-sensitive learning (scale_pos_weight) and leaf-wise tree growth, the production model achieves an elite ~49.1% Recall at a strict 5% False Positive Rate (FPR). It maximizes the detection of fraudulent applications while minimizing the number of legitimate customers flagged for manual review. + +## Clone + +To replicate this pipeline locally, you will need to clone the repository and set up your MinIO environment variables. + +``` +git clone +``` + +Once your .Renviron is configured with your BAF_KEY, BAF_SECRET, and BAF_ENDPOINT, you can execute the entire DAG: + + +## Acknowledgements + +This project utilizes the Bank Account Fraud (BAF) dataset, originally published and presented at NeurIPS 2022. It is a massive, privacy-preserving suite of realistic tabular data designed specifically for evaluating fairness and performance in machine learning fraud detection. + +## Citation diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..71953e8 --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,57 @@ +url: https://docs.robwiederstein.org/baflakehouse + +template: + bootstrap: 5 + bootswatch: flatly # Clean, professional look + +navbar: + structure: + left: [intro, reference, articles, presentation] + components: + presentation: + text: "Slides" + icon: fa-person-chalkboard + href: slides/index.html + +reference: + - title: "Data Ingestion & Lakehouse Setup" + desc: "Functions for moving data from CSV to partitioned Parquet in MinIO." + contents: + - baflakehouse-package + - convert_to_parquet + - connect_baf + - clean_baf_base + + - title: "Feature Engineering & Preprocessing" + desc: "The 'Recipes' layer of the pipeline." + contents: + - engineer_features + - prepare_eda_recipe + - build_baf_recipe # NEW: Untrained blueprint for production + - generate_model_inputs + + - title: "The Tournament (Model Selection)" + desc: "Cross-validation and imbalance strategy testing." + contents: + - run_imbalance_tournament + - train_diag_model + - create_efficiency_plot # Moved here: Belongs with the tournament + + - title: "Final Evaluation & Production Deployment" + desc: "Results on unseen data (Months 6-7) and MinIO artifact serialization." + contents: + - evaluate_final_model + - train_production_model # NEW: The final deployment function + + - title: "Reporting: Tables & Visualizations" + desc: "Generating ggplot2 figures and gt tables for Quarto." + contents: + - starts_with("plot_") + - starts_with("compute_") + - starts_with("format_") # Neatly catches all your gt table formatters + + - title: "Pipeline Utilities" + desc: "Internal helpers for the targets workflow and slide generation." + contents: + - starts_with("save_report_") + - render_slides # Consolidated here \ No newline at end of file diff --git a/_quarto.yml b/_quarto.yml new file mode 100644 index 0000000..48fce31 --- /dev/null +++ b/_quarto.yml @@ -0,0 +1,24 @@ +project: + type: default + output-dir: reports/slides + render: + - index.qmd + +format: + revealjs: + theme: simple + slide-number: true + incremental: true + controls: true +bibliography: references.bib +csl: ieee.csl + +execute: + echo: false + warning: false + message: false + +knitr: + opts_chunk: + out.width: "100%" + fig.align: "center" \ No newline at end of file diff --git a/_targets.R b/_targets.R new file mode 100644 index 0000000..91be568 --- /dev/null +++ b/_targets.R @@ -0,0 +1,327 @@ +library(targets) +library(tarchetypes) + +tar_option_set( + packages = c( + "arrow", + "bonsai", + "duckdb", + "glue", + "gt", + "here", + "lightgbm", + "lubridate", + "tidymodels", + "tidyverse", + "cowplot", + "colorspace", + "readr", + "scales", + "ggplot2", + "quarto", + "corrr", + "recipes", + "themis", + "tidyselect" + ) +) + +tar_source("./R/functions.R") + +list( + tar_target( + baf_parquet_prefix, + convert_to_parquet( + from_prefix = "01_raw", + to_prefix = "02_intermediate", + bucket_name = "baf-fraud" + ) + ), + + tar_target( + baf_primary_prefix, + clean_baf_base( + in_prefix = baf_parquet_prefix, + out_prefix = "03_primary/variant=Base", + bucket_name = "baf-fraud", + partitioning = "month", + existing_data_behavior = "delete_matching", + verbose = TRUE + ) + ), + + tar_target( + baf_feature_prefix, + engineer_features( + in_prefix = baf_primary_prefix, + out_prefix = "04_feature/variant=Base", + bucket_name = "baf-fraud", + partitioning = "month", + existing_data_behavior = "delete_matching", + verbose = TRUE + ) + ), + + # ---- Figure objects ---- + tar_target( + fig_fraud_by_month, + plot_fraud_by_month(baf_primary_prefix, bucket_name = "baf-fraud") + ), + + # ---- Saved figure path (file target) ---- + tar_target( + fig_fraud_by_month_path, + save_report_figure( + fig_fraud_by_month, + filename = "fig_fraud_by_month.png", + out_dir = "reports/figures" + ), + format = "file" + ), + tar_target( + tbl_fraud_by_month_data, + compute_fraud_by_month(baf_primary_prefix) + ), + + tar_target( + tbl_fraud_by_month_gt, + format_fraud_by_month_gt(tbl_fraud_by_month_data) + ), + + tar_target( + tbl_fraud_by_month_path, + save_report_table(tbl_fraud_by_month_gt, filename = "tbl_fraud_by_month.rds"), + format = "file" + ), + + # ---- Exploratory Data Analysis (EDA) Layer ---- + tar_target( + data_eda_m0, + connect_baf(baf_primary_prefix, use_duckdb = TRUE) |> + filter(month == 0) |> + collect() + ), + + tar_target( + eda_recipe, + prepare_eda_recipe(data_eda_m0) + ), + + tar_target( + data_baked_eda_m0, + bake(eda_recipe, new_data = data_eda_m0) + ), + + tar_target( + model_diag, + train_diag_model(data_baked_eda_m0) + ), + + # ---- EDA Figures ---- + tar_target(fig_var_imp, plot_var_imp(model_diag)), + tar_target(fig_hexbin_interaction, plot_hexbin_interaction(data_baked_eda_m0)), + tar_target(fig_missingness, plot_missingness(data_eda_m0)), + tar_target(fig_num_cor, plot_num_cor(data_eda_m0)), + + # ---- Saved EDA Figure Paths ---- + tar_target( + fig_var_imp_path, + save_report_figure(fig_var_imp, "fig_var_imp.png"), + format = "file" + ), + tar_target( + fig_hexbin_interaction_path, + save_report_figure(fig_hexbin_interaction, "fig_hexbin_interaction.png"), + format = "file" + ), + tar_target( + fig_missingness_path, + save_report_figure(fig_missingness, "fig_missingness.png"), + format = "file" + ), + tar_target( + fig_num_cor_path, + save_report_figure(fig_num_cor, "fig_num_cor.png"), + format = "file" + ), + + # ---- 05_model_input Generation ---- + tar_target( + model_inputs_prefix, + generate_model_inputs( + feature_prefix = baf_feature_prefix, + out_prefix = "05_model_input", + bucket_name = "baf-fraud" + ) + ), + + # ---- Tournament Inputs ---- + tar_target( + imbalance_tasks, + tibble::tribble( + ~recipe_name, ~data_folder, ~scale_pos_weight, + "Standard", "baseline", 1, + "Weighted", "baseline", 4, + "Under", "under", 1, + "Smote", "smote", 1, + "Adasyn", "adasyn", 1, + "Tomek", "tomek", 1 + ) + ), + + tar_target( + imbalance_windows, + tibble::tribble( + ~window_id, ~train_months, ~test_month, + "Window 1", c(0, 1, 2), 3, + "Window 2", c(1, 2, 3), 4, + "Window 3", c(2, 3, 4), 5 + ) + ), + + # ---- 1. Data Layer (The Tournament Results) ---- + tar_target( + tbl_strategy_showdown, + { + # Force DAG to wait for the folders to be generated + force(model_inputs_prefix) + # Pass baf_feature_prefix so it tracks the latest layer + run_imbalance_tournament(imbalance_tasks, imbalance_windows, baf_feature_prefix) + } + ), + + # ---- 2. Figure Layer ---- + tar_target( + fig_strategy_showdown, + create_efficiency_plot(tbl_strategy_showdown) + ), + + tar_target( + fig_strategy_showdown_path, + save_report_figure( + fig_strategy_showdown, + filename = "fig_strategy_showdown.png", + out_dir = "reports/figures" + ), + format = "file" + ), + + # ---- 3. Table Layer (gt object) ---- + tar_target( + tbl_strategy_showdown_gt, + format_class_imbalance_tourney_gt(tbl_strategy_showdown) + ), + + tar_target( + tbl_strategy_showdown_path, + save_report_table( + tbl_strategy_showdown_gt, + filename = "tbl_strategy_showdown.rds", + out_dir = "reports/tables" + ), + format = "file" + ), + + # ---- Final Production Evaluation ---- + tar_target( + final_eval_data, + evaluate_final_model(params = winning_params) + ), + + tar_target( + final_conf_mat, + yardstick::conf_mat(final_eval_data, truth, pred_class) + ), + + tar_target( + final_roc_curve, + yardstick::roc_curve(final_eval_data, truth, prob) + ), + + tar_target( + final_pr_curve, + yardstick::pr_curve(final_eval_data, truth, prob) + ), + + # ---- Save Final Assets ---- + tar_target( + fig_final_curves, + { + p1 <- ggplot2::autoplot(final_roc_curve) + ggplot2::labs(title = "ROC Curve (Months 6-7)") + p2 <- ggplot2::autoplot(final_pr_curve) + ggplot2::labs(title = "PR Curve (Months 6-7)") + cowplot::plot_grid(p1, p2) + } + ), + + tar_target( + fig_final_curves_path, + save_report_figure(fig_final_curves, "fig_final_curves.png"), + format = "file" + ), + + tar_target( + tbl_final_conf_mat_path, + save_report_table(final_conf_mat, "tbl_final_conf_mat.rds", out_dir = "reports/tables"), + format = "file" + ), + # ---- Generate and Save Heatmap ---- + tar_target( + fig_final_conf_mat, + plot_conf_mat_heatmap(final_conf_mat) + ), + + tar_target( + fig_final_conf_mat_path, + save_report_figure(fig_final_conf_mat, "fig_final_conf_mat.png"), + format = "file" + ), + # ---- Report Dependency Update ---- + tar_target( + report_assets, + c( + fig_fraud_by_month_path, + tbl_fraud_by_month_path, + fig_strategy_showdown_path, + tbl_strategy_showdown_path, + fig_var_imp_path, + fig_hexbin_interaction_path, + fig_missingness_path, + fig_num_cor_path + ), + format = "file" + ), + + tar_quarto( + report_slides, + path = "index.qmd" + ), + # production model deployment + tar_target( + data_full, + connect_baf(baf_feature_prefix, use_duckdb = TRUE) |> + collect() + ), + tar_target( + production_recipe_blueprint, + build_baf_recipe(data_full) + ), + tar_target( + winning_params, + list( + trees = 844, + tree_depth = 3, + learn_rate = 0.0204, + min_n = 389 + ) + ), + tar_target( + production_model_uri, + train_production_model( + data = data_full, + recipe = production_recipe_blueprint, # <--- Pass the untrained blueprint! + best_params = winning_params, + model_filename = "baf_lgbm_prod_v1.txt" + ), + format = "rds" + ) +) \ No newline at end of file diff --git a/deploy.R b/deploy.R new file mode 100644 index 0000000..5e44f11 --- /dev/null +++ b/deploy.R @@ -0,0 +1,22 @@ +# deploy.R + +message("📝 1. Updating package documentation and namespace...") +devtools::document() + +message("🚀 2. Running targets pipeline...") +targets::tar_make() + +message("🏗️ 3. Building pkgdown website...") +pkgdown::build_site() + +message("📦 4. Injecting slides into public docs/ folder...") +# Create target directories +fs::dir_create("docs/slides/reports/figures") + +# Copy the HTML and index_files +fs::dir_copy("reports/slides/", "docs/slides/", overwrite = TRUE) + +# Copy the figures so the slides can see them +fs::dir_copy("reports/figures/", "docs/slides/reports/figures/", overwrite = TRUE) + +message("✅ DONE: Site and slides successfully deployed to docs/") \ No newline at end of file diff --git a/ieee.csl b/ieee.csl new file mode 100644 index 0000000..7612c15 --- /dev/null +++ b/ieee.csl @@ -0,0 +1,519 @@ + + diff --git a/index.qmd b/index.qmd new file mode 100644 index 0000000..45ba317 --- /dev/null +++ b/index.qmd @@ -0,0 +1,364 @@ +--- +title: "BAF Fraud Modeling" +author: "Rob Wiederstein" +date: today +date-format: long +--- + +```{r} +#| label: setup +#| include: false +library(here) +library(targets) +library(knitr) + +# Make chunk paths resolve relative to reports/ +#knitr::opts_knit$set(root.dir = here::here("reports")) + +# Declare deps for tar_quarto() (optional, but good) +invisible(targets::tar_read(report_assets)) +``` + +# Introduction + +## Bank Account Fraud Dataset{.incremental} + +- Synthetic online account applications +- 1M rows (Base) +- 8 months (0–7) +- Base + 5 biased variants +- Label: Fraud vs Legit +- Fraud $\approx 1\%$ + +:::{.notes} +**What it is (plain English):** each row is a bank account opening application submitted online. Fraudsters may impersonate someone (identity theft) or invent a person; once approved they quickly exploit the credit line or use the account to move illicit funds. + +**Why it exists:** the BAF *suite* was created as a large, realistic benchmark to stress-test ML performance and fairness under **dynamic / drifting** conditions and “extreme” class imbalance. The variants introduce controlled bias patterns; the Base set has no induced bias. + +**How it was made:** the released data are **synthetic** (generated from a CTGAN trained on an anonymized, feature-engineered real dataset). Privacy protections mean no row corresponds to a real identifiable person. + +**Time structure:** `month` ranges 0–7 (eight months). This is why we use chronological evaluation (train early months, test late months). + +**Target variable:** datasheet label is `fraud_bool` (0/1). In our pipeline we rename/recode to `outcome` with labels “Legit” and “Fraud” for readability. +::: + +## Typical Scenario{.incremental} + +Fraudsters will + +1. Impersonate someone or + +2. Create fake identity then + +3. Max out the line or + +4. receive illicit payment + + +## Data Cleaning{.incremental} + +- Relabel outcome. +- -1 → NA. +- Negative amount → NA. +- Write clean Parquet. + +:::{.notes} +**Outcome** +- `fraud_bool` (0/1) → `outcome` ("Legit"/"Fraud"); drop `fraud_bool`. + +**Missing encoded as values** +- Recode `-1` to `NA` for: + - `prev_address_months_count` + - `current_address_months_count` + - `bank_months_count` + - `session_length_in_minutes` + - `device_distinct_emails` (your data uses `device_distinct_emails_8w`; function handles either name) + +**Range constraint** +- `intended_balcon_amount < 0` → `NA` (negative values are missing-encoding). + +**Output** +- Saved cleaned dataset as Parquet under `03_primary/variant=Base/` partitioned by `month`. +::: + +# Explore + +## Variable Importance + +```{r} +#| label: fig-var-imp +#| fig-cap: "Top 15 features driving the diagnostic model." +knitr::include_graphics("reports/figures/fig_var_imp.png") +``` + +:::{.notes} +The diagnostic LightGBM model shows that behavior and identity structure dominate the early splits. +::: + +## Feature Interaction + +```{r} +#| label: fig-hexbin-interaction +#| fig-cap: "Interaction between Credit Risk Score and Address History." +knitr::include_graphics("reports/figures/fig_hexbin_interaction.png") +``` + +:::{.notes} +Fraud clusters noticeably in high credit risk profiles combined with specific address tenure patterns. +::: + +## Missingness Signal + +```{r} +#| label: fig-missingness +#| fig-cap: "Missingness rates by outcome." +knitr::include_graphics("reports/figures/fig_missingness.png") +``` + +:::{.notes} +Fraudsters are systematically omitting key tenure details (like previous address and bank history) compared to legitimate applicants. +::: + +## Numeric Correlation + +```{r} +#| label: fig-num-cor +#| fig-cap: "Core numeric correlation matrix." +knitr::include_graphics("reports/figures/fig_num_cor.png") +``` + +:::{.notes} +The structural anchor of the synthetic data is visible here, particularly the relationship between credit score and proposed limit. +::: + +# LightGBM + +## About {.incremental} + +- Originally released in 2016 +- Maintained by Microsoft +- Over 18,000 stars on GitHub +- King of Kaggle for tabular data +- Announcing paper over 23,000 citations +- Sped up similar gradient boosting algorithms 20x + + +## Academic Support + +::: {.panel-tabset} + +### Standard + +>For tabular supervised learning, gradient boosted decision trees—most notably XGBoost and LightGBM—are strong, low-latency baselines because they exploit hand-engineered behavioral features; LightGBM remains a **standard** reference point for card and e-commerce fraud tasks [@aminian_fraudtransformer_2025] + +### Accurate + +>[W]e found that the LightGBM approach had the highest detection **accuracy** of fraudulent activity with 97% in the experiments conducted. An additional key objective of reducing false alerts was accomplished, as the number of false alarms went from 13,024 to 6,249[@iscan_walletbased_2023] + +### Efficient + +>[W]e choose LightGBM as the base machine learning model due to its **efficiency** and widespread use in handling large-scale and structured datasets, particularly in financial domains such as credit card fraud detection.[@zhao_improved_2024] + + +::: + +# Unbalanced Classes + +## The Challenge + +
+
+ +>The scarce occurrences of rare events impair the detection task … + + +:::{.notes} +**Citation:** Guo, H., Li, Y., Shang, J., Gu, M., Huang, Y., & Gong, B. (2017). +*Learning from class-imbalanced data: Review of methods and applications.* +**Expert Systems with Applications, 73**, 220–239. https://doi.org/10.1016/j.eswa.2016.12.035 +::: + +## Bank Fraud Prevalence + +```{r} +#| label: fig-fraud-prevalence-plot +#| fig-cap: "Fraudulent versus legitimate applications by month." +knitr::include_graphics("reports/figures/fig_fraud_by_month.png") +``` + +:::{.notes} +Fraud represents approximately one percent of applications. +::: + +## Fraud Prevalence + +```{r} +#| label: tbl-fraud-by-month +#| tbl-cap: "Something" +readRDS("reports/tables/tbl_fraud_by_month.rds") +``` + + +## Methods Tested{.incremental} + +- **Standard:** Baseline (No sampling). +- **Weighted:** Cost-sensitive learning ($4\times$ penalty). +- **Undersampling:** Random removal of majority class. +- **SMOTE:** Synthetic Minority Over-sampling Technique. +- **ADASYN:** Adaptive Synthetic Sampling (hard examples). +- **Tomek Links:** Cleaning boundary ambiguity. + +:::{.notes} +**Standard:** The control group. We let the gradient booster handle the 1% imbalance naturally. + +**Weighted:** We used `scale_pos_weight` to tell LightGBM that missing a Fraud case is 4x worse than a false alarm. + +**Undersampling:** We threw away about 75% of the Legit cases to balance the ratio. Fast, but risky. + +**SMOTE & ADASYN:** The "heavy hitters." These generate fake fraud data based on nearest neighbors. Adasyn focuses specifically on "hard to learn" fraud cases. + +**Tomek:** A cleaning method that removes Legit cases that are "too close" to Fraud cases, theoretically making the decision boundary clearer. +::: + +## Strategy Showdown: Results + +```{r} +#| label: tbl-strategy-showdown +#| tbl-cap: "Performance comparison across imbalance strategies using 3-month rolling windows." +readRDS("reports/tables/tbl_strategy_showdown.rds") +``` + +:::{.notes} +The "Standard" baseline is statistically indistinguishable from more complex methods like SMOTE and Adasyn (p > 0.05). Complex sampling provides no significant predictive gain for this dataset. +::: + +## Sampling Compared + +```{r} +#| label: fig-strategy-showdown +#| fig-cap: "PR-AUC performance versus computational training time." +knitr::include_graphics("reports/figures/fig_strategy_showdown.png") +``` + +:::{.notes} +The Standard strategy represents the "Efficient Frontier." It achieves near-peak performance while being nearly twice as fast as SMOTE or Adasyn. Tomek sampling actually degraded performance while increasing compute time. +::: + +## Sampling Methods Discarded {.incremental} + +- No statistical gain + +- Resource intensive + +- Scalability + +:::{.notes} + +Complex sampling methods like SMOTE and Adasyn do not outperform the baseline "Standard" model, as shown by their non-significant p-values (p > 0.05). + +Synthetic generation and neighbor calculations nearly double the average training time per fold compared to the standard approach. + +For larger file sizes, simplicity helps avoid memory bottlenecks and excessive compute costs. + +Future model performance gains should focus in places other than sampling techniques. +::: + +# Feature Creation + +# Final Results + +## The Confusion Matrix + +```{r} +#| label: fig-confusion-matrix +#| echo: false +#| out-width: "100%" +knitr::include_graphics("resources/images/confusion-matrix.png") +``` + +:::{.notes} +The confusion matrix is the foundation of all classification metrics. Every metric we care about is derived from these four cells. + +In the fraud context: +- **TN:** Legitimate application correctly approved. No harm done. +- **FP:** Legitimate application flagged as fraud. Customer friction, potential churn. +- **FN:** Fraud case missed. Direct financial loss — the costliest error. +- **TP:** Fraud correctly caught. The goal. + +The key insight: not all errors are equal. A missed fraud case (FN) costs far more than a false alarm (FP). Our threshold and metric choices reflect this asymmetry. +::: + +## Precision & Recall + +
+ +$$\text{Recall} = \frac{TP}{TP + FN}$$ + +> Of all actual frauds, how many did we catch? + +$$\text{Precision} = \frac{TP}{TP + FP}$$ + +> Of all flagged cases, how many were real fraud? + +:::{.notes} +**Recall** (also called **detection rate**) is the primary metric for fraud detection. Missing a fraud case (FN) is costly, so we want Recall as high as possible. A model that flags every application gets a perfect detection rate — but at the cost of Precision. + +**Precision** captures that cost: if we flag everything, every legitimate customer gets rejected. Precision measures how trustworthy our fraud flags actually are. + +The **Precision-Recall tradeoff** is the core tension in fraud modeling. Lowering the decision threshold increases Recall (catch more fraud) but decreases Precision (more false alarms). The right balance depends on the operational cost of each error type. + +Our model targets **~49% Recall at a 5% False Positive Rate** — a deliberate operating point chosen to limit customer friction while catching nearly half of fraud. +::: + +## ROC vs Precision-Recall AUC + +::: {.panel-tabset} + +### ROC AUC + +- Plots **Recall** vs **False Positive Rate** +- AUC = 0.5 is random; 1.0 is perfect +- Optimistic under class imbalance +- Inflated by the large TN pool + +### PR AUC + +- Plots **Precision** vs **Recall** +- Focuses entirely on the minority class +- Harder to game with a large Legit majority +- Preferred metric for fraud detection + +::: + +:::{.notes} +**Why ROC AUC can mislead on imbalanced data:** with 99% legitimate applications, even a naive model achieves a low False Positive Rate simply because the TN pool is enormous. ROC AUC rewards this, making models look better than they are. + +**PR AUC** ignores true negatives entirely. It only asks: of the positive (fraud) predictions, how precise were we, and how much fraud did we recall? This makes it a far more honest scoreboard when positives are rare. + +**Rule of thumb:** use ROC AUC for balanced classes; use PR AUC for imbalanced fraud/anomaly detection tasks. We report both, but optimise for PR AUC. +::: + +## Final Model Evaluation + +```{r} +#| label: fig-conf-mat-heatmap +#| echo: false +#| out-width: "100%" +#| fig-cap: "Confusion Matrix Heatmap (5% Decision Threshold)" +knitr::include_graphics("reports/figures/fig_final_conf_mat.png") +``` + +## Diagnostic Metrics + +```{r} +#| label: fig-final-curves +#| echo: false +#| out-width: "100%" +#| fig-cap: "ROC and Precision-Recall Curves for Out-of-Sample Data" +knitr::include_graphics("reports/figures/fig_final_curves.png") +``` + +# References {.smaller} + + + diff --git a/man/baflakehouse-package.Rd b/man/baflakehouse-package.Rd new file mode 100644 index 0000000..69bcb6e --- /dev/null +++ b/man/baflakehouse-package.Rd @@ -0,0 +1,11 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/baflakehouse-package.R +\docType{_PACKAGE} +\name{baflakehouse-package} +\alias{baflakehouse-package} +\title{baflakehouse: Lakehouse Workflow for the Bank Account Fraud Dataset} +\description{ +Tools to ingest the Bank Account Fraud (BAF) Base dataset into a MinIO/S3-backed +lakehouse, clean encoded missing values, and produce reproducible reporting +artifacts orchestrated with targets. +} diff --git a/man/build_baf_recipe.Rd b/man/build_baf_recipe.Rd new file mode 100644 index 0000000..a45610a --- /dev/null +++ b/man/build_baf_recipe.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{build_baf_recipe} +\alias{build_baf_recipe} +\title{Build Untrained BAF Recipe} +\usage{ +build_baf_recipe(data) +} +\arguments{ +\item{data}{A data frame} +} +\value{ +An untrained tidymodels recipe +} +\description{ +Build Untrained BAF Recipe +} diff --git a/man/clean_baf_base.Rd b/man/clean_baf_base.Rd new file mode 100644 index 0000000..1150bcb --- /dev/null +++ b/man/clean_baf_base.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{clean_baf_base} +\alias{clean_baf_base} +\title{Clean the BAF Base dataset and write to 03_primary} +\usage{ +clean_baf_base( + in_prefix, + out_prefix = "03_primary/variant=Base", + bucket_name = "baf-fraud", + partitioning = "month", + existing_data_behavior = c("overwrite", "error", "delete_matching"), + verbose = TRUE +) +} +\arguments{ +\item{in_prefix}{Character. Input dataset prefix inside bucket (e.g. "02_intermediate/variant=Base").} + +\item{out_prefix}{Character. Output dataset prefix inside bucket (e.g. "03_primary/variant=Base").} + +\item{bucket_name}{Character. Bucket name. Default "baf-fraud".} + +\item{partitioning}{Character vector of columns to partition by. Default "month". Set NULL to disable.} + +\item{existing_data_behavior}{One of "overwrite", "error", "delete_matching". Default "overwrite".} + +\item{verbose}{Logical. Emit progress messages. Default TRUE.} +} +\value{ +Character. out_prefix (for downstream targets). +} +\description{ +Clean the BAF Base dataset and write to 03_primary +} diff --git a/man/compute_fraud_by_month.Rd b/man/compute_fraud_by_month.Rd new file mode 100644 index 0000000..3f17ca5 --- /dev/null +++ b/man/compute_fraud_by_month.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{compute_fraud_by_month} +\alias{compute_fraud_by_month} +\title{Fraud prevalence by month (counts + percent)} +\usage{ +compute_fraud_by_month(in_prefix, use_duckdb = TRUE) +} +\arguments{ +\item{in_prefix}{Character. Dataset prefix inside the bucket, e.g. "03_primary/variant=Base".} + +\item{use_duckdb}{Logical. Use DuckDB for lazy querying. Default TRUE.} +} +\value{ +A tibble with Month, Fraud, Legit, Total, Pct_Fraud. +} +\description{ +Computes monthly counts of Fraud/Legit, totals, and percent fraud. +} diff --git a/man/connect_baf.Rd b/man/connect_baf.Rd new file mode 100644 index 0000000..fc5569d --- /dev/null +++ b/man/connect_baf.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{connect_baf} +\alias{connect_baf} +\title{Connect to BAF dataset on MinIO (Arrow or DuckDB)} +\usage{ +connect_baf(prefix, bucket_name = Sys.getenv("BAF_BUCKET"), use_duckdb = TRUE) +} +\arguments{ +\item{prefix}{Character. Dataset prefix inside the bucket +(e.g., "02_intermediate/variant=Base").} + +\item{bucket_name}{Character. Bucket name. Defaults to env var BAF_BUCKET.} + +\item{use_duckdb}{Logical. If TRUE, return a DuckDB-backed lazy tbl.} +} +\value{ +An Arrow Dataset (default) or a DuckDB-backed lazy table. +} +\description{ +Connect to BAF dataset on MinIO (Arrow or DuckDB) +} diff --git a/man/convert_to_parquet.Rd b/man/convert_to_parquet.Rd new file mode 100644 index 0000000..0a22081 --- /dev/null +++ b/man/convert_to_parquet.Rd @@ -0,0 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{convert_to_parquet} +\alias{convert_to_parquet} +\title{Convert BAF CSV to partitioned Parquet in MinIO (S3)} +\usage{ +convert_to_parquet(from_prefix, to_prefix, bucket_name = "baf-fraud") +} +\arguments{ +\item{from_prefix}{Character. Prefix/key under the bucket containing CSVs (e.g. \code{"01_raw"}).} + +\item{to_prefix}{Character. Prefix/key under the bucket to write Parquet dataset (e.g. \code{"02_intermediate"}).} + +\item{bucket_name}{Character. Bucket name. Default \code{"baf-fraud"}.} +} +\value{ +A character string giving the destination dataset prefix (typically \code{to_prefix}). +} +\description{ +Reads \code{Base.csv} from a MinIO/S3 bucket prefix (e.g., \code{"01_raw"}) and writes a +Hive-style partitioned Parquet dataset to another prefix (e.g., \code{"02_intermediate"}), +partitioned by \code{variant} (e.g., \verb{variant=Base/part-*.parquet}). +} +\details{ +Connection settings are taken from environment variables: +\itemize{ +\item \code{BAF_ENDPOINT} (e.g. \code{"minio:9000"} or \code{"192.168.4.xx:9000"}) +\item \code{BAF_KEY} (MinIO access key) +\item \code{BAF_SECRET} (MinIO secret key) +} +} +\examples{ +\dontrun{ +Sys.setenv( + BAF_ENDPOINT = "minio:9000", + BAF_KEY = "YOUR_ACCESS_KEY", + BAF_SECRET = "YOUR_SECRET_KEY" +) +convert_to_parquet(from_prefix = "01_raw", to_prefix = "02_intermediate", bucket_name = "baf-fraud") +} +} diff --git a/man/create_efficiency_plot.Rd b/man/create_efficiency_plot.Rd new file mode 100644 index 0000000..47ae669 --- /dev/null +++ b/man/create_efficiency_plot.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{create_efficiency_plot} +\alias{create_efficiency_plot} +\title{Create Effectiveness vs Efficiency Plot} +\usage{ +create_efficiency_plot(results_df) +} +\arguments{ +\item{results_df}{Tibble from run_imbalance_tournament} +} +\description{ +Create Effectiveness vs Efficiency Plot +} diff --git a/man/engineer_features.Rd b/man/engineer_features.Rd new file mode 100644 index 0000000..7e4436e --- /dev/null +++ b/man/engineer_features.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{engineer_features} +\alias{engineer_features} +\title{Engineer features for the BAF dataset} +\usage{ +engineer_features( + in_prefix = "03_primary/variant=Base", + out_prefix = "04_feature/variant=Base", + bucket_name = "baf-fraud", + partitioning = "month", + existing_data_behavior = "delete_matching", + verbose = TRUE +) +} +\arguments{ +\item{in_prefix}{Character. Input dataset prefix (e.g., "03_primary/variant=Base").} + +\item{out_prefix}{Character. Output dataset prefix (e.g., "04_feature/variant=Base").} + +\item{bucket_name}{Character. The S3/MinIO bucket name. Default "baf-fraud".} + +\item{partitioning}{Character vector. Columns to partition by. Default "month".} + +\item{existing_data_behavior}{Character. Behavior when data exists. Default "delete_matching".} + +\item{verbose}{Logical. Whether to print progress messages. Default TRUE.} +} +\value{ +Character. The output prefix path for downstream targets. +} +\description{ +Reads the primary BAF dataset and engineers new features, such as +\code{n_missing}, which counts the number of missing values across key +tenure and financial columns. This calculation is performed out-of-memory +using Arrow compute. +} diff --git a/man/evaluate_final_model.Rd b/man/evaluate_final_model.Rd new file mode 100644 index 0000000..a4f7c1e --- /dev/null +++ b/man/evaluate_final_model.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{evaluate_final_model} +\alias{evaluate_final_model} +\title{Final Model Evaluation (Months 6 & 7)} +\usage{ +evaluate_final_model( + params, + bucket_name = "baf-fraud", + inputs_prefix = "05_model_input" +) +} +\arguments{ +\item{params}{A named list of LightGBM hyperparameters with elements: +\code{trees}, \code{tree_depth}, \code{learn_rate}, \code{loss_reduction}, \code{min_n}.} + +\item{bucket_name}{Character. Bucket name. Default "baf-fraud".} + +\item{inputs_prefix}{Character. Model input prefix. Default "05_model_input".} +} +\value{ +A tibble with columns \code{truth}, \code{prob}, and \code{pred_class}. +} +\description{ +Trains the winning strategy on the full training set (Months 0-5) +and evaluates it on the unseen test set (Months 6-7). +} diff --git a/man/format_class_imbalance_tourney_gt.Rd b/man/format_class_imbalance_tourney_gt.Rd new file mode 100644 index 0000000..fd46aef --- /dev/null +++ b/man/format_class_imbalance_tourney_gt.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{format_class_imbalance_tourney_gt} +\alias{format_class_imbalance_tourney_gt} +\title{Format Class Imbalance Tournament Table} +\usage{ +format_class_imbalance_tourney_gt(results_df) +} +\arguments{ +\item{results_df}{The tibble output from \code{run_imbalance_tournament}.} +} +\value{ +A formatted gt table object. +} +\description{ +Aggregates results from the model tournament and performs paired t-tests +against the 'Standard' model to determine statistical significance. +} diff --git a/man/format_fraud_by_month_gt.Rd b/man/format_fraud_by_month_gt.Rd new file mode 100644 index 0000000..19e8c4a --- /dev/null +++ b/man/format_fraud_by_month_gt.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{format_fraud_by_month_gt} +\alias{format_fraud_by_month_gt} +\title{Format fraud-by-month table as a gt object} +\usage{ +format_fraud_by_month_gt(x) +} +\arguments{ +\item{x}{Tibble from compute_fraud_by_month().} +} +\value{ +A gt table. +} +\description{ +Format fraud-by-month table as a gt object +} diff --git a/man/generate_model_inputs.Rd b/man/generate_model_inputs.Rd new file mode 100644 index 0000000..6856f31 --- /dev/null +++ b/man/generate_model_inputs.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{generate_model_inputs} +\alias{generate_model_inputs} +\title{Generate Resampled Model Inputs} +\usage{ +generate_model_inputs( + feature_prefix = "04_feature/variant=Base", + out_prefix = "05_model_input", + bucket_name = "baf-fraud" +) +} +\arguments{ +\item{feature_prefix}{Character. Input prefix (e.g., "04_feature/variant=Base").} + +\item{out_prefix}{Character. Output prefix base (e.g., "05_model_input").} + +\item{bucket_name}{Character. Bucket name. Default "baf-fraud".} +} +\value{ +Character. The output prefix (for targets dependency tracking). +} +\description{ +Reads the engineered feature layer, prepares a base tidymodels recipe, +and generates resampled datasets (Baseline, Under, SMOTE, Adasyn, Tomek) +across all months, saving them to the 05_model_input prefix. +} diff --git a/man/plot_conf_mat_heatmap.Rd b/man/plot_conf_mat_heatmap.Rd new file mode 100644 index 0000000..8293381 --- /dev/null +++ b/man/plot_conf_mat_heatmap.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{plot_conf_mat_heatmap} +\alias{plot_conf_mat_heatmap} +\title{Plot Confusion Matrix Heatmap} +\usage{ +plot_conf_mat_heatmap(cm, title = "") +} +\arguments{ +\item{cm}{A yardstick conf_mat object.} + +\item{title}{Character. The main title of the plot.} + +\item{subtitle}{Character. The subtitle of the plot.} +} +\value{ +A ggplot object. +} +\description{ +Generates a styled 4-quadrant heatmap from a yardstick confusion matrix. +} diff --git a/man/plot_fraud_by_month.Rd b/man/plot_fraud_by_month.Rd new file mode 100644 index 0000000..fe1ffe6 --- /dev/null +++ b/man/plot_fraud_by_month.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{plot_fraud_by_month} +\alias{plot_fraud_by_month} +\title{Plot applications by month (Legit vs Fraud) on a log scale} +\usage{ +plot_fraud_by_month( + dataset_prefix, + bucket_name = "baf-fraud", + palette = "Dark 3", + title = "" +) +} +\arguments{ +\item{dataset_prefix}{Character. Prefix inside the bucket, e.g. "03_primary/variant=Base".} + +\item{bucket_name}{Character. Bucket name. Default "baf-fraud".} + +\item{palette}{Character. colorspace qualitative palette name. Default "Dark 3".} + +\item{title}{Character. Plot title. Default "".} +} +\value{ +A ggplot object. +} +\description{ +Builds an exploratory chart of absolute application counts by month +split by outcome (Legit vs Fraud). Uses a log10 y-axis so rare fraud +remains visible on the same axis. +} +\details{ +Data source: expects a cleaned "primary" dataset prefix (e.g. 03_primary/variant=Base) +stored in MinIO/S3, accessed via \code{connect_baf()}. +} diff --git a/man/plot_hexbin_interaction.Rd b/man/plot_hexbin_interaction.Rd new file mode 100644 index 0000000..1aa0fda --- /dev/null +++ b/man/plot_hexbin_interaction.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{plot_hexbin_interaction} +\alias{plot_hexbin_interaction} +\title{Plot Hexbin Interaction} +\usage{ +plot_hexbin_interaction(baked_data, title = "") +} +\arguments{ +\item{baked_data}{Baked EDA data} + +\item{title}{Character. Plot title. Default "".} +} +\description{ +Plot Hexbin Interaction +} diff --git a/man/plot_missingness.Rd b/man/plot_missingness.Rd new file mode 100644 index 0000000..c657cb3 --- /dev/null +++ b/man/plot_missingness.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{plot_missingness} +\alias{plot_missingness} +\title{Plot Missingness Signal} +\usage{ +plot_missingness(eda_data, title = "") +} +\arguments{ +\item{eda_data}{Raw EDA data} + +\item{title}{Character. Plot title. Default "".} +} +\description{ +Plot Missingness Signal +} diff --git a/man/plot_num_cor.Rd b/man/plot_num_cor.Rd new file mode 100644 index 0000000..bc45745 --- /dev/null +++ b/man/plot_num_cor.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{plot_num_cor} +\alias{plot_num_cor} +\title{Plot Numeric Correlation Matrix} +\usage{ +plot_num_cor(eda_data, title = "") +} +\arguments{ +\item{eda_data}{Raw EDA data} + +\item{title}{Character. Plot title. Default "".} +} +\description{ +Plot Numeric Correlation Matrix +} diff --git a/man/plot_var_imp.Rd b/man/plot_var_imp.Rd new file mode 100644 index 0000000..5c14e2a --- /dev/null +++ b/man/plot_var_imp.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{plot_var_imp} +\alias{plot_var_imp} +\title{Plot Variable Importance} +\usage{ +plot_var_imp(model, title = "") +} +\arguments{ +\item{model}{Trained LightGBM model} + +\item{title}{Character. Plot title. Default "".} +} +\description{ +Plot Variable Importance +} diff --git a/man/prepare_eda_recipe.Rd b/man/prepare_eda_recipe.Rd new file mode 100644 index 0000000..7eae699 --- /dev/null +++ b/man/prepare_eda_recipe.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{prepare_eda_recipe} +\alias{prepare_eda_recipe} +\title{Prepare EDA Recipe} +\usage{ +prepare_eda_recipe(eda_data) +} +\arguments{ +\item{eda_data}{Raw EDA data} +} +\description{ +Prepare EDA Recipe +} diff --git a/man/render_slides.Rd b/man/render_slides.Rd new file mode 100644 index 0000000..6d50783 --- /dev/null +++ b/man/render_slides.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{render_slides} +\alias{render_slides} +\title{Render Quarto revealjs slideshow after required assets exist} +\usage{ +render_slides(qmd = "index.qmd", assets, output_dir = "reports/slides") +} +\arguments{ +\item{qmd}{Character. Input Quarto file (e.g. "index.qmd").} + +\item{assets}{Character vector. File paths that must exist before rendering.} + +\item{output_dir}{Character. Output directory for rendered slides.} +} +\value{ +Character path to the rendered HTML file. +} +\description{ +Render Quarto revealjs slideshow after required assets exist +} diff --git a/man/run_imbalance_tournament.Rd b/man/run_imbalance_tournament.Rd new file mode 100644 index 0000000..cc0442d --- /dev/null +++ b/man/run_imbalance_tournament.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{run_imbalance_tournament} +\alias{run_imbalance_tournament} +\title{Run Class Imbalance Tournament} +\usage{ +run_imbalance_tournament( + tasks, + windows, + feature_prefix, + bucket_name = "baf-fraud", + inputs_prefix = "05_model_input" +) +} +\arguments{ +\item{tasks}{A tibble containing recipe_name, data_folder, and scale_pos_weight.} + +\item{windows}{A tibble containing window_id, train_months, and test_month.} + +\item{feature_prefix}{Character. The upstream dependency prefix (used to force DAG execution).} + +\item{bucket_name}{Character. Bucket name. Default "baf-fraud".} + +\item{inputs_prefix}{Character. The folder containing the sampled data. Default "05_model_input".} +} +\value{ +A tibble with the summarized tournament results. +} +\description{ +Trains LightGBM models across different class imbalance strategies +(Standard, SMOTE, Adasyn, etc.) using sliding time windows. Evaluates +performance using PR-AUC and calculates statistical significance. +Includes common-sense hyperparameter defaults to prevent overfitting. +} diff --git a/man/save_report_figure.Rd b/man/save_report_figure.Rd new file mode 100644 index 0000000..357124a --- /dev/null +++ b/man/save_report_figure.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{save_report_figure} +\alias{save_report_figure} +\title{Save a report figure artifact} +\usage{ +save_report_figure( + plot, + filename, + out_dir = "reports/figures", + width = 12, + height = 6.75, + dpi = 300 +) +} +\arguments{ +\item{plot}{A ggplot object.} + +\item{filename}{Character. Output filename, e.g. \code{"fig_fraud_by_month.png"}.} + +\item{out_dir}{Character. Output directory. Default \code{"reports/figures"}.} + +\item{width, height, dpi}{Numeric. Passed to \code{ggplot2::ggsave()}.} +} +\value{ +Character. Normalized path to the saved file. +} +\description{ +Saves a ggplot object to \code{reports/figures/}. +Intended for use in \code{targets} pipelines as a file-producing target. +} diff --git a/man/save_report_table.Rd b/man/save_report_table.Rd new file mode 100644 index 0000000..71adb18 --- /dev/null +++ b/man/save_report_table.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{save_report_table} +\alias{save_report_table} +\title{Save a report table artifact} +\usage{ +save_report_table(x, filename, out_dir = "reports/tables") +} +\arguments{ +\item{x}{Object to save.} + +\item{filename}{Output filename, e.g. "tbl_fraud_by_month.rds".} + +\item{out_dir}{Output directory. Default "reports/tables".} +} +\value{ +Character path to saved file. +} +\description{ +Save a report table artifact +} diff --git a/man/train_diag_model.Rd b/man/train_diag_model.Rd new file mode 100644 index 0000000..4aad842 --- /dev/null +++ b/man/train_diag_model.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{train_diag_model} +\alias{train_diag_model} +\title{Train Diagnostic Model} +\usage{ +train_diag_model(baked_data) +} +\arguments{ +\item{baked_data}{Baked EDA data} +} +\description{ +Train Diagnostic Model +} diff --git a/man/train_production_model.Rd b/man/train_production_model.Rd new file mode 100644 index 0000000..456f275 --- /dev/null +++ b/man/train_production_model.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{train_production_model} +\alias{train_production_model} +\title{Train and Serialize Production LightGBM Model} +\usage{ +train_production_model( + data, + recipe, + best_params, + model_filename = "lgbm_prod.txt" +) +} +\arguments{ +\item{data}{A data frame containing the full BAF dataset (Months 0-7).} + +\item{recipe}{A prepared tidymodels recipe.} + +\item{best_params}{A list or tibble of the winning hyperparameters.} + +\item{model_filename}{Character. The target filename. Defaults to "lgbm_prod.txt".} +} +\value{ +Character. The MinIO URI of the uploaded model artifact. +} +\description{ +Trains a LightGBM model on the complete dataset using the winning +hyperparameters, serializes it to a text file, and uploads it directly +to MinIO via the Apache Arrow S3 interface. +} diff --git a/references.bib b/references.bib new file mode 100644 index 0000000..1e332a3 --- /dev/null +++ b/references.bib @@ -0,0 +1,154 @@ +@misc{_bankaccountfraud_, + title = {Bank-Account-Fraud/Documents/Datasheet.Pdf at Main {$\cdot$} Feedzai/Bank-Account-Fraud}, + journal = {GitHub}, + urldate = {2026-02-11}, + abstract = {Supporting documentation for the paper \"Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation\", and the Bank Account Fraud suite of datasets. - feedzai/bank-...}, + howpublished = {https://github.com/feedzai/bank-account-fraud/blob/main/documents/datasheet.pdf}, + langid = {english}, + file = {/home/rkw/Zotero/storage/LT4CJB34/datasheet.html} +} + +@article{ali2013classification, + title = {Classification with Class Imbalance Problem}, + author = {Ali, Aida and Shamsuddin, Siti Mariyam and Ralescu, Anca L}, + year = 2013, + journal = {Int. J. Advance Soft Compu. Appl}, + volume = {5}, + number = {3}, + pages = {176--204}, + keywords = {class imbalance,unbalanced classes}, + file = {/home/rkw/Zotero/storage/3AVBB4SQ/Ali et al. - 2013 - Classification with class imbalance problem.pdf} +} + +@misc{aminian_fraudtransformer_2025, + title = {{{FraudTransformer}}: {{Time-Aware GPT}} for {{Transaction Fraud Detection}}}, + shorttitle = {{{FraudTransformer}}}, + author = {Aminian, Gholamali and Elliott, Andrew and Li, Tiger and Wong, Timothy Cheuk Hin and Dehon, Victor Claude and Szpruch, Lukasz and Maple, Carsten and Read, Christopher and Brown, Martin and Reinert, Gesine and Mamouei, Mo}, + year = 2025, + month = oct, + number = {arXiv:2509.23712}, + eprint = {2509.23712}, + primaryclass = {cs}, + publisher = {arXiv}, + doi = {10.48550/arXiv.2509.23712}, + urldate = {2026-02-21}, + abstract = {Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransformer, a sequence model that augments a vanilla GPT-style architecture with (i) a dedicated time encoder that embeds either absolute timestamps or inter-event values, and (ii) a learned positional encoder that preserves relative order. Experiments on a large industrial dataset -- tens of millions of transactions and auxiliary events -- show that FraudTransformer surpasses four strong classical baselines (Logistic Regression, XGBoost and LightGBM) as well as transformer ablations that omit either the time or positional component. On the held-out test set it delivers the highest AUROC and PRAUC.}, + archiveprefix = {arXiv}, + keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}, + file = {/home/rkw/Zotero/storage/YQQE72ZK/Aminian et al. - 2025 - FraudTransformer Time-Aware GPT for Transaction Fraud Detection.pdf;/home/rkw/Zotero/storage/XVL3X42S/2509.html} +} + +@article{bartoszkrawczyk_learning_2016, + title = {Learning from Imbalanced Data: Open Challenges and Future Directions}, + author = {{Bartosz Krawczyk} and Krawczyk, Bartosz}, + year = 2016, + month = apr, + journal = {Progress in Artificial Intelligence}, + volume = {5}, + number = {4}, + pages = {221--232}, + doi = {10.1007/s13748-016-0094-0}, + abstract = {Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.}, + keywords = {imbalanced data,unbalanced classes}, + annotation = {MAG ID: 2338318698}, + file = {/home/rkw/Zotero/storage/ZFYYHYYR/Bartosz Krawczyk and Krawczyk - 2016 - Learning from imbalanced data open challenges and future directions.pdf} +} + +@article{iscan_walletbased_2023, + title = {Wallet-{{Based Transaction Fraud Prevention Through LightGBM With}} the {{Focus}} on {{Minimizing False Alarms}}}, + author = {Iscan, Can and Kumas, Osman and Akbulut, Fatma Patlar and Akbulut, Akhan}, + year = 2023, + journal = {IEEE Access}, + volume = {11}, + pages = {131465--131474}, + issn = {2169-3536}, + doi = {10.1109/ACCESS.2023.3321666}, + urldate = {2026-02-21}, + abstract = {E-wallets' rising popularity can be attributed to the fact that they facilitate a wide variety of financial activities such as payments, transfers, investments, etc., and eliminate the need for actual cash or cards. The confidentiality, availability, and integrity of a user's financial information stored in an electronic wallet can be compromised by threats such as phishing, malware, and social engineering; therefore, fintech platforms employ intelligent fraud detection mechanisms to mitigate the problem. The purpose of this study is to detect fraudulent activity using cutting-edge machine learning techniques on data obtained from the leading e-wallet platform in Turkey. After a comprehensive analysis of the dataset's features via feature engineering procedures, we found that the LightGBM approach had the highest detection accuracy of fraudulent activity with 97\% in the experiments conducted. An additional key objective of reducing false alerts was accomplished, as the number of false alarms went from 13,024 to 6,249. This approach resulted in the establishment of a machine-learning model suitable for use by relatively small fraud detection teams.}, + keywords = {E-wallet,Feature extraction,fintech,Fraud,fraud detection,LightGBM,Machine learning,Machine learning algorithms,Monitoring,Online banking,Real-time systems}, + file = {/home/rkw/Zotero/storage/B2K3D8W9/Iscan et al. - 2023 - Wallet-Based Transaction Fraud Prevention Through LightGBM With the Focus on Minimizing False Alarms.pdf} +} + +@article{jesus_baf_, + title = {{{BAF Dataset Suite Datasheet}}}, + author = {Jesus, S{\'e}rgio and Pombal, Jos{\'e} and Alves, Duarte and Cruz, Andr{\'e} F and Saleiro, Pedro and Ribeiro, Rita P and Gama, Jo{\~a}o and Bizarro, Pedro}, + langid = {english}, + file = {/home/rkw/Zotero/storage/6A29JS3R/Jesus et al. - BAF Dataset Suite Datasheet.pdf} +} + +@misc{jesus_turning_2022, + title = {Turning the {{Tables}}: {{Biased}}, {{Imbalanced}}, {{Dynamic Tabular Datasets}} for {{ML Evaluation}}}, + shorttitle = {Turning the {{Tables}}}, + author = {Jesus, S{\'e}rgio and Pombal, Jos{\'e} and Alves, Duarte and Cruz, Andr{\'e} and Saleiro, Pedro and Ribeiro, Rita P. and Gama, Jo{\~a}o and Bizarro, Pedro}, + year = 2022, + month = nov, + number = {arXiv:2211.13358}, + eprint = {2211.13358}, + primaryclass = {cs}, + publisher = {arXiv}, + doi = {10.48550/arXiv.2211.13358}, + urldate = {2026-02-11}, + abstract = {Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks. However, tabular data -- which is prevalent in many high-stakes domains -- has been lagging behind. To bridge this gap, we present Bank Account Fraud (BAF), the first publicly available privacy-preserving, large-scale, realistic suite of tabular datasets. The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized,real-world bank account opening fraud detection dataset. This setting carries a set of challenges that are commonplace in real-world applications, including temporal dynamics and significant class imbalance. Additionally, to allow practitioners to stress test both performance and fairness of ML methods, each dataset variant of BAF contains specific types of data bias. With this resource, we aim to provide the research community with a more realistic, complete, and robust test bed to evaluate novel and existing methods.}, + archiveprefix = {arXiv}, + keywords = {Computer Science - Machine Learning}, + file = {/home/rkw/Zotero/storage/FSBNDIP4/Jesus et al. - 2022 - Turning the Tables Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation.pdf;/home/rkw/Zotero/storage/7HXKQPDC/2211.html} +} + +@article{johnson_deep_2019, + title = {Deep {{Learning}} and {{Data Sampling}} with {{Imbalanced Big Data}}}, + author = {Johnson, Justin M. and Khoshgoftaar, Taghi M.}, + year = 2019, + month = jul, + pages = {175--183}, + doi = {10.1109/iri.2019.00038}, + abstract = {This study evaluates the use of deep learning and data sampling on a class-imbalanced Big Data problem, i.e. Medicare fraud detection. Medicare offers affordable health insurance to the elderly population and serves more than 15\% of the United States population. To increase transparency and help reduce fraud, the Centers for Medicare and Medicaid Services (CMS) have made several data sets publicly available for analysis. Our research group has conducted several studies using CMS data and traditional machine learning algorithms (non-deep learning), but challenges associated with severe class imbalance leave room for improvement. These previous studies serve as baselines as we employ deep neural networks with various data-sampling techniques to determine the efficacy of deep learning in addressing class imbalance. Random over-sampling (ROS), random under-sampling (RUS), and combinations of the two (ROS-RUS) are applied to study how varying levels of class imbalance impact model training and performance. Classwise performance is maximized by identifying optimal decision thresholds, and a strong linear relationship between minority class size and optimal threshold is observed. Results show that ROS significantly outperforms RUS, combining RUS and ROS both maximizes performance and efficiency with a 4 x speedup in training time, and the default threshold of 0.5 is never optimal when training data is imbalanced. To the best of our knowledge, this is the first study to provide statistical results comparing ROS, RUS, and ROS-RUS deep learning methods across a range of class distributions. Additional contributions include a unique analysis of thresholding as it relates to the minority class size and state-of-the-art performance on the given fraud detection task.}, + keywords = {LEIE}, + annotation = {MAG ID: 2974916584}, + file = {/home/rkw/Zotero/storage/ZQR6NJPU/Johnson and Khoshgoftaar - 2019 - Deep Learning and Data Sampling with Imbalanced Bi.pdf} +} + +@article{kaur_systematic_2019, + title = {A {{Systematic Review}} on {{Imbalanced Data Challenges}} in {{Machine Learning}}: {{Applications}} and {{Solutions}}}, + shorttitle = {A {{Systematic Review}} on {{Imbalanced Data Challenges}} in {{Machine Learning}}}, + author = {Kaur, Harsurinder and Pannu, Husanbir Singh and Malhi, Avleen Kaur}, + year = 2019, + month = aug, + journal = {ACM Comput. Surv.}, + volume = {52}, + number = {4}, + pages = {79:1--79:36}, + issn = {0360-0300}, + doi = {10.1145/3343440}, + urldate = {2026-02-11}, + abstract = {In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.}, + file = {/home/rkw/Zotero/storage/4WZYQG9W/Kaur et al. - 2019 - A Systematic Review on Imbalanced Data Challenges in Machine Learning Applications and Solutions.pdf} +} + +@inproceedings{zhang_leveraging_2025, + title = {Leveraging {{LightGBM}} for {{High-Accuracy Telecom Fraud Detection}} with {{Clustering-Based Undersampling}}}, + booktitle = {2025 8th {{International Symposium}} on {{Big Data}} and {{Applied Statistics}} ({{ISBDAS}})}, + author = {Zhang, Shuo and Zhang, Bo and Hou, Shichong and Fu, Zhiyuan}, + year = 2025, + month = feb, + pages = {384--388}, + doi = {10.1109/ISBDAS64762.2025.11117117}, + urldate = {2026-02-21}, + abstract = {This study presents a machine learning framework designed to predict and prevent telecom fraud by analyzing 1 million transaction records. The model identifies critical fraud patterns through the analysis of key features, including PIN usage, transaction frequency, and location. To address the severe class imbalance in the dataset (initially a 10:1 ratio), a clustering-based undersampling technique was employed, balancing the dataset to a 1:1 ratio while preserving data integrity and improving model performance. The framework utilizes LightGBM, optimized through Bayesian hyperparameter tuning and five-fold cross-validation, achieving an accuracy of 98\% and a robust AUC of 0.9. Key findings highlight that transactions involving both bank cards and PIN verification exhibit a drastically reduced fraud risk (0.0001 ratio), while cardless or PIN-less transactions are significantly more susceptible to fraud. The study emphasizes the importance of multi-factor authentication and provides actionable insights for financial institutions to mitigate fraud risks. Additionally, it underscores the transformative potential of machine learning in real-time fraud detection, with future opportunities for integrating emerging technologies like blockchain to further enhance security.}, + keywords = {Accuracy,Bayes methods,Bayesian optimization,Clustering-Based Undersampling,Fraud,fraud prediction,LightGBM model,machine learning,Machine learning,Pins,Predictive models,Real-time systems,Security,Telecommunications,Tuning}, + file = {/home/rkw/Zotero/storage/8XIZ5CCT/11117117.html} +} + +@article{zhao_improved_2024, + title = {Improved {{LightGBM}} for {{Extremely Imbalanced Data}} and {{Application}} to {{Credit Card Fraud Detection}}}, + author = {Zhao, Xiaosong and Liu, Yong and Zhao, Qiangfu}, + year = 2024, + journal = {IEEE Access}, + volume = {12}, + pages = {159316--159335}, + issn = {2169-3536}, + doi = {10.1109/ACCESS.2024.3487212}, + urldate = {2026-02-21}, + abstract = {Credit card fraud (CCF) is a significant threat to cardholders and financial institutions. CCF detection against this threat is challenging due to extremely imbalanced data (EID). EID involves extremely few instances of fraud for training and an extremely high risk of overlooking fraud. While class balancing or oversampling techniques can address the former problem by punishing negative classes or augmenting the positive data, they do not mitigate the latter. In contrast, the cost-sensitive learning approach targets only the high risk of false negative errors. Therefore, existing approaches are insufficient to solve all the issues of the EID problem. Based on the LightGBM (Light Gradient Boosting Machine) framework, this study introduces two novel machine-learning methods: the class balancing cost-harmonization LightGBM (CB-CHL-LightGBM) and the oversampling cost-harmonization LightGBM (OS-CHL-LightGBM). The new approaches combine class balancing or oversampling technology with LightGBM to solve the EID problem comprehensively. They enhance the efficacy of LightGBM in CCF detection scenarios. Experimental results on three CCF datasets indicate that the two proposed methods outperform LightGBM in several crucial performance metrics. For example, compared with the original LightGBM, CB-CHL-LightGBM or OS-CHL-LightGBM can increase the F2-score from 0.77 to 0.83 for the first dataset, from 0.77 to 0.86 for the second dataset, and from 0.70 to 0.82 for the third dataset. However, adding class balancing, oversampling, and cost-harmonization loss separately to LightGBM may not obtain better results.}, + keywords = {Accuracy,Boosting,Class balancing cost-harmonization LightGBM,Classification algorithms,cost-sensitive,Costs,credit card fraud detection,Credit cards,Data models,extremely imbalanced data,Fraud,interpretability,Loss measurement,oversampling,Synthetic data,Training}, + file = {/home/rkw/Zotero/storage/KI2Y7NIA/Zhao et al. - 2024 - Improved LightGBM for Extremely Imbalanced Data and Application to Credit Card Fraud Detection.pdf} +} diff --git a/renv.lock b/renv.lock new file mode 100644 index 0000000..551efb3 --- /dev/null +++ b/renv.lock @@ -0,0 +1,10634 @@ +{ + "R": { + "Version": "4.5.2", + "Repositories": [ + { + "Name": "P3M", + "URL": "https://packagemanager.posit.co/cran/latest" + }, + { + "Name": "CRAN", + "URL": "https://cloud.r-project.org" + } + ] + }, + "Packages": { + "BH": { + "Package": "BH", + "Version": "1.90.0-1", + "Source": "Repository", + "Type": "Package", + "Title": "Boost C++ Header Files", + "Date": "2025-12-13", + "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"John W.\", \"Emerson\", role = \"aut\"), person(\"Michael J.\", \"Kane\", role = \"aut\", comment = c(ORCID = \"0000-0003-1899-6662\")))", + "Description": "Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN packages. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. 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Kane [aut] (ORCID: )", + "Maintainer": "Dirk Eddelbuettel ", + "Repository": "P3M", + "Encoding": "UTF-8" + }, + "DBI": { + "Package": "DBI", + "Version": "1.2.3", + "Source": "Repository", + "Title": "R Database Interface", + "Date": "2024-06-02", + "Authors@R": "c( person(\"R Special Interest Group on Databases (R-SIG-DB)\", role = \"aut\"), person(\"Hadley\", \"Wickham\", role = \"aut\"), person(\"Kirill\", \"Müller\", , \"kirill@cynkra.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-1416-3412\")), person(\"R Consortium\", role = \"fnd\") )", + "Description": "A database interface definition for communication between R and relational database management systems. 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See file COPYRIGHT for details\") )", + "Description": "Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric.", + "License": "GPL (>= 3)", + "URL": "https://github.com/jefferislab/RANN, https://jefferislab.github.io/RANN/", + "BugReports": "https://github.com/jefferislab/RANN/issues", + "Suggests": [ + "testthat" + ], + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "yes", + "Author": "Gregory Jefferis [aut, cre] (), Samuel E. Kemp [aut], Kirill Müller [ctb] (), Sunil Arya [aut, cph] (), David Mount [aut, cph] (), University of Maryland [cph] (ANN library is copyright University of Maryland and Sunil Arya and David Mount. 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Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.", + "License": "GPL-2", + "NeedsCompilation": "no", + "Repository": "P3M", + "Encoding": "UTF-8" + }, + "Rcpp": { + "Package": "Rcpp", + "Version": "1.1.1", + "Source": "Repository", + "Title": "Seamless R and C++ Integration", + "Date": "2026-01-07", + "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Romain\", \"Francois\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"JJ\", \"Allaire\", role = \"aut\", comment = c(ORCID = \"0000-0003-0174-9868\")), person(\"Kevin\", \"Ushey\", role = \"aut\", comment = c(ORCID = \"0000-0003-2880-7407\")), person(\"Qiang\", \"Kou\", role = \"aut\", comment = c(ORCID = \"0000-0001-6786-5453\")), person(\"Nathan\", \"Russell\", role = \"aut\"), person(\"Iñaki\", \"Ucar\", role = \"aut\", comment = c(ORCID = \"0000-0001-6403-5550\")), person(\"Doug\", \"Bates\", role = \"aut\", comment = c(ORCID = \"0000-0001-8316-9503\")), person(\"John\", \"Chambers\", role = \"aut\"))", + "Description": "The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. 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Models include gradient boosted decision trees with 'LightGBM' (Ke et al, 2017.), conditional inference trees and conditional random forests with 'partykit' (Hothorn and Zeileis, 2015. and Hothorn et al, 2006. ), and accelerated oblique random forests with 'aorsf' (Jaeger et al, 2022 ).", + "License": "MIT + file LICENSE", + "URL": "https://bonsai.tidymodels.org/, https://github.com/tidymodels/bonsai", + "BugReports": "https://github.com/tidymodels/bonsai/issues", + "Depends": [ + "parsnip (>= 1.0.1)", + "R (>= 4.1)" + ], + "Imports": [ + "cli", + "dials", + "dplyr", + "purrr", + "rlang (>= 1.1.0)", + "stats", + "tibble", + "utils", + "withr" + ], + "Suggests": [ + "aorsf (>= 0.1.5)", + "covr", + "knitr", + "lightgbm", + "Matrix", + "modeldata", + "partykit", + "rmarkdown", + "rsample", + "testthat (>= 3.0.0)", + "tune" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-25", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Daniel Falbel [aut], Athos Damiani [aut], Roel M. 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Brazzale ", + "Note": "Maintainers are not available to give advice on using a package they did not author.", + "Description": "Functions and datasets for bootstrapping from the book \"Bootstrap Methods and Their Application\" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.", + "Title": "Bootstrap Functions", + "Depends": [ + "R (>= 3.0.0)", + "graphics", + "stats" + ], + "Suggests": [ + "MASS", + "survival" + ], + "LazyData": "yes", + "ByteCompile": "yes", + "License": "Unlimited", + "NeedsCompilation": "no", + "Author": "Angelo Canty [aut] (author of original code for S), Brian Ripley [aut, trl] (conversion to R, maintainer 1999--2022, author of parallel support), Alessandra R. 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Capabilities include: date-time parsing, formatting, arithmetic, extraction and updating of components, and rounding.", + "License": "MIT + file LICENSE", + "URL": "https://clock.r-lib.org, https://github.com/r-lib/clock", + "BugReports": "https://github.com/r-lib/clock/issues", + "Depends": [ + "R (>= 4.0.0)" + ], + "Imports": [ + "cli (>= 3.6.4)", + "lifecycle (>= 1.0.4)", + "rlang (>= 1.1.5)", + "tzdb (>= 0.5.0)", + "vctrs (>= 0.6.5)" + ], + "Suggests": [ + "covr", + "knitr", + "magrittr", + "pillar", + "rmarkdown", + "slider (>= 0.3.2)", + "testthat (>= 3.0.0)", + "withr" + ], + "LinkingTo": [ + "cpp11 (>= 0.5.2)", + "tzdb (>= 0.5.0)" + ], + "VignetteBuilder": "knitr", + "Config/build/compilation-database": "true", + "Config/Needs/website": "lubridate, tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "yes", + "Author": "Davis Vaughan [aut, cre], Posit Software, PBC [cph, fnd]", + "Maintainer": "Davis Vaughan ", + "Repository": "P3M" + }, + "cluster": { + "Package": "cluster", + "Version": "2.1.8.1", + "Source": "Repository", + "VersionNote": "Last CRAN: 2.1.8 on 2024-12-10; 2.1.7 on 2024-12-06; 2.1.6 on 2023-11-30; 2.1.5 on 2023-11-27", + "Date": "2025-03-11", + "Priority": "recommended", + "Title": "\"Finding Groups in Data\": Cluster Analysis Extended Rousseeuw et al.", + "Description": "Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) \"Finding Groups in Data\".", + "Maintainer": "Martin Maechler ", + "Authors@R": "c(person(\"Martin\",\"Maechler\", role = c(\"aut\",\"cre\"), email=\"maechler@stat.math.ethz.ch\", comment = c(ORCID = \"0000-0002-8685-9910\")) ,person(\"Peter\", \"Rousseeuw\", role=\"aut\", email=\"peter.rousseeuw@kuleuven.be\", comment = c(\"Fortran original\", ORCID = \"0000-0002-3807-5353\")) ,person(\"Anja\", \"Struyf\", role=\"aut\", comment= \"S original\") ,person(\"Mia\", \"Hubert\", role=\"aut\", email= \"Mia.Hubert@uia.ua.ac.be\", comment = c(\"S original\", ORCID = \"0000-0001-6398-4850\")) ,person(\"Kurt\", \"Hornik\", role=c(\"trl\", \"ctb\"), email=\"Kurt.Hornik@R-project.org\", comment=c(\"port to R; maintenance(1999-2000)\", ORCID=\"0000-0003-4198-9911\")) ,person(\"Matthias\", \"Studer\", role=\"ctb\") ,person(\"Pierre\", \"Roudier\", role=\"ctb\") ,person(\"Juan\", \"Gonzalez\", role=\"ctb\") ,person(\"Kamil\", \"Kozlowski\", role=\"ctb\") ,person(\"Erich\", \"Schubert\", role=\"ctb\", comment = c(\"fastpam options for pam()\", ORCID = \"0000-0001-9143-4880\")) ,person(\"Keefe\", \"Murphy\", role=\"ctb\", comment = \"volume.ellipsoid({d >= 3})\") #not yet ,person(\"Fischer-Rasmussen\", \"Kasper\", role = \"ctb\", comment = \"Gower distance for CLARA\") )", + "Depends": [ + "R (>= 3.5.0)" + ], + "Imports": [ + "graphics", + "grDevices", + "stats", + "utils" + ], + "Suggests": [ + "MASS", + "Matrix" + ], + "SuggestsNote": "MASS: two examples using cov.rob() and mvrnorm(); Matrix tools for testing", + "Enhances": [ + "mvoutlier", + "fpc", + "ellipse", + "sfsmisc" + ], + "EnhancesNote": "xref-ed in man/*.Rd", + "LazyLoad": "yes", + "LazyData": "yes", + "ByteCompile": "yes", + "BuildResaveData": "no", + "License": "GPL (>= 2)", + "URL": "https://svn.r-project.org/R-packages/trunk/cluster/", + "NeedsCompilation": "yes", + "Author": "Martin Maechler [aut, cre] (), Peter Rousseeuw [aut] (Fortran original, ), Anja Struyf [aut] (S original), Mia Hubert [aut] (S original, ), Kurt Hornik [trl, ctb] (port to R; maintenance(1999-2000), ), Matthias Studer [ctb], Pierre Roudier [ctb], Juan Gonzalez [ctb], Kamil Kozlowski [ctb], Erich Schubert [ctb] (fastpam options for pam(), ), Keefe Murphy [ctb] (volume.ellipsoid({d >= 3}))", + "Repository": "CRAN" + }, + "codetools": { + "Package": "codetools", + "Version": "0.2-20", + "Source": "Repository", + "Priority": "recommended", + "Author": "Luke Tierney ", + "Description": "Code analysis tools for R.", + "Title": "Code Analysis Tools for R", + "Depends": [ + "R (>= 2.1)" + ], + "Maintainer": "Luke Tierney ", + "URL": "https://gitlab.com/luke-tierney/codetools", + "License": "GPL", + "NeedsCompilation": "no", + "Repository": "CRAN" + }, + "colorspace": { + "Package": "colorspace", + "Version": "2.1-2", + "Source": "Repository", + "Date": "2025-09-22", + "Title": "A Toolbox for Manipulating and Assessing Colors and Palettes", + "Authors@R": "c(person(given = \"Ross\", family = \"Ihaka\", role = \"aut\", email = \"ihaka@stat.auckland.ac.nz\"), person(given = \"Paul\", family = \"Murrell\", role = \"aut\", email = \"paul@stat.auckland.ac.nz\", comment = c(ORCID = \"0000-0002-3224-8858\")), person(given = \"Kurt\", family = \"Hornik\", role = \"aut\", email = \"Kurt.Hornik@R-project.org\", comment = c(ORCID = \"0000-0003-4198-9911\")), person(given = c(\"Jason\", \"C.\"), family = \"Fisher\", role = \"aut\", email = \"jfisher@usgs.gov\", comment = c(ORCID = \"0000-0001-9032-8912\")), person(given = \"Reto\", family = \"Stauffer\", role = \"aut\", email = \"Reto.Stauffer@uibk.ac.at\", comment = c(ORCID = \"0000-0002-3798-5507\")), person(given = c(\"Claus\", \"O.\"), family = \"Wilke\", role = \"aut\", email = \"wilke@austin.utexas.edu\", comment = c(ORCID = \"0000-0002-7470-9261\")), person(given = c(\"Claire\", \"D.\"), family = \"McWhite\", role = \"aut\", email = \"claire.mcwhite@utmail.utexas.edu\", comment = c(ORCID = \"0000-0001-7346-3047\")), person(given = \"Achim\", family = \"Zeileis\", role = c(\"aut\", \"cre\"), email = \"Achim.Zeileis@R-project.org\", comment = c(ORCID = \"0000-0003-0918-3766\")))", + "Description": "Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, ).", + "Depends": [ + "R (>= 3.0.0)", + "methods" + ], + "Imports": [ + "graphics", + "grDevices", + "stats" + ], + "Suggests": [ + "datasets", + "utils", + "KernSmooth", + "MASS", + "kernlab", + "mvtnorm", + "vcd", + "tcltk", + "shiny", + "shinyjs", + "ggplot2", + "dplyr", + "scales", + "grid", + "png", + "jpeg", + "knitr", + "rmarkdown", + "RColorBrewer", + "rcartocolor", + "scico", + "viridis", + "wesanderson" + ], + "VignetteBuilder": "knitr", + "License": "BSD_3_clause + file LICENSE", + "URL": "https://colorspace.R-Forge.R-project.org/, https://hclwizard.org/", + "BugReports": "https://colorspace.R-Forge.R-project.org/contact.html", + "LazyData": "yes", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "yes", + "Author": "Ross Ihaka [aut], Paul Murrell [aut] (ORCID: ), Kurt Hornik [aut] (ORCID: ), Jason C. Fisher [aut] (ORCID: ), Reto Stauffer [aut] (ORCID: ), Claus O. Wilke [aut] (ORCID: ), Claire D. McWhite [aut] (ORCID: ), Achim Zeileis [aut, cre] (ORCID: )", + "Maintainer": "Achim Zeileis ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "colourpicker": { + "Package": "colourpicker", + "Version": "1.3.0", + "Source": "Repository", + "Title": "A Colour Picker Tool for Shiny and for Selecting Colours in Plots", + "Authors@R": "c( person(\"Dean\", \"Attali\", email = \"daattali@gmail.com\", role = c(\"aut\", \"cre\"), comment= c(ORCID=\"0000-0002-5645-3493\")), person(\"David\", \"Griswold\", email=\"novachild@gmail.com\", role = \"ctb\") )", + "Description": "A colour picker that can be used as an input in 'Shiny' apps or Rmarkdown documents. The colour picker supports alpha opacity, custom colour palettes, and many more options. A Plot Colour Helper tool is available as an 'RStudio' Addin, which helps you pick colours to use in your plots. A more generic Colour Picker 'RStudio' Addin is also provided to let you select colours to use in your R code.", + "URL": "https://github.com/daattali/colourpicker, https://daattali.com/shiny/colourInput/", + "BugReports": "https://github.com/daattali/colourpicker/issues", + "Depends": [ + "R (>= 3.1.0)" + ], + "Imports": [ + "ggplot2", + "htmltools", + "htmlwidgets (>= 0.7)", + "jsonlite", + "miniUI (>= 0.1.1)", + "shiny (>= 0.11.1)", + "shinyjs (>= 2.0.0)", + "utils" + ], + "Suggests": [ + "knitr (>= 1.7)", + "rmarkdown", + "rstudioapi (>= 0.5)", + "shinydisconnect" + ], + "License": "MIT + file LICENSE", + "Encoding": "UTF-8", + "RoxygenNote": "7.2.3", + "NeedsCompilation": "no", + "Author": "Dean Attali [aut, cre] (), David Griswold [ctb]", + "Maintainer": "Dean Attali ", + "Repository": "P3M" + }, + "commonmark": { + "Package": "commonmark", + "Version": "2.0.0", + "Source": "Repository", + "Type": "Package", + "Title": "High Performance CommonMark and Github Markdown Rendering in R", + "Authors@R": "c( person(\"Jeroen\", \"Ooms\", ,\"jeroenooms@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"John MacFarlane\", role = \"cph\", comment = \"Author of cmark\"))", + "Description": "The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text.", + "License": "BSD_2_clause + file LICENSE", + "URL": "https://docs.ropensci.org/commonmark/ https://ropensci.r-universe.dev/commonmark", + "BugReports": "https://github.com/r-lib/commonmark/issues", + "Suggests": [ + "curl", + "testthat", + "xml2" + ], + "RoxygenNote": "7.3.2", + "Language": "en-US", + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Author": "Jeroen Ooms [aut, cre] (ORCID: ), John MacFarlane [cph] (Author of cmark)", + "Maintainer": "Jeroen Ooms ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "conflicted": { + "Package": "conflicted", + "Version": "1.2.0", + "Source": "Repository", + "Title": "An Alternative Conflict Resolution Strategy", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", role = c(\"aut\", \"cre\")), person(\"RStudio\", role = c(\"cph\", \"fnd\")) )", + "Description": "R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use.", + "License": "MIT + file LICENSE", + "URL": "https://conflicted.r-lib.org/, https://github.com/r-lib/conflicted", + "BugReports": "https://github.com/r-lib/conflicted/issues", + "Depends": [ + "R (>= 3.2)" + ], + "Imports": [ + "cli (>= 3.4.0)", + "memoise", + "rlang (>= 1.0.0)" + ], + "Suggests": [ + "callr", + "covr", + "dplyr", + "Matrix", + "methods", + "pkgload", + "testthat (>= 3.0.0)", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.2.3", + "NeedsCompilation": "no", + "Author": "Hadley Wickham [aut, cre], RStudio [cph, fnd]", + "Maintainer": "Hadley Wickham ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "corrr": { + "Package": "corrr", + "Version": "0.4.5", + "Source": "Repository", + "Type": "Package", + "Title": "Correlations in R", + "Authors@R": "c( person(\"Max\", \"Kuhn\", , \"max@rstudio.com\", role = c(\"aut\", \"cre\")), person(\"Simon\", \"Jackson\", role = \"aut\"), person(\"Jorge\", \"Cimentada\", role = \"aut\") )", + "Description": "A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualizing the matrix in terms of the strength of the correlations.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/tidymodels/corrr, https://corrr.tidymodels.org", + "BugReports": "https://github.com/tidymodels/corrr/issues", + "Depends": [ + "R (>= 3.4)" + ], + "Imports": [ + "dplyr (>= 1.0.0)", + "ggplot2 (>= 2.2.0)", + "ggrepel (>= 0.6.5)", + "glue (>= 1.4.2)", + "purrr (>= 0.2.2)", + "rlang (>= 0.4.0)", + "seriation (>= 1.2-0)", + "tibble (>= 2.0)" + ], + "Suggests": [ + "covr", + "DBI", + "dbplyr (>= 1.2.1)", + "knitr (>= 1.13)", + "rmarkdown (>= 0.9.6)", + "RSQLite", + "sparklyr (>= 0.9)", + "testthat (>= 3.0.0)" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "Config/testthat/edition": "3", + "NeedsCompilation": "no", + "Author": "Max Kuhn [aut, cre], Simon Jackson [aut], Jorge Cimentada [aut]", + "Maintainer": "Max Kuhn ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "cowplot": { + "Package": "cowplot", + "Version": "1.2.0", + "Source": "Repository", + "Title": "Streamlined Plot Theme and Plot Annotations for 'ggplot2'", + "Authors@R": "person( given = \"Claus O.\", family = \"Wilke\", role = c(\"aut\", \"cre\"), email = \"wilke@austin.utexas.edu\", comment = c(ORCID = \"0000-0002-7470-9261\") )", + "Description": "Provides various features that help with creating publication-quality figures with 'ggplot2', such as a set of themes, functions to align plots and arrange them into complex compound figures, and functions that make it easy to annotate plots and or mix plots with images. The package was originally written for internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package). 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Some knowledge of 'libcurl' is recommended; for a more-user-friendly web client see the 'httr2' package which builds on this package with http specific tools and logic.", + "License": "MIT + file LICENSE", + "SystemRequirements": "libcurl (>= 7.73): libcurl-devel (rpm) or libcurl4-openssl-dev (deb)", + "URL": "https://jeroen.r-universe.dev/curl", + "BugReports": "https://github.com/jeroen/curl/issues", + "Suggests": [ + "spelling", + "testthat (>= 1.0.0)", + "knitr", + "jsonlite", + "later", + "rmarkdown", + "httpuv (>= 1.4.4)", + "webutils" + ], + "VignetteBuilder": "knitr", + "Depends": [ + "R (>= 3.0.0)" + ], + "RoxygenNote": "7.3.2", + "Encoding": "UTF-8", + "Language": "en-US", + "NeedsCompilation": "yes", + "Author": "Jeroen Ooms [aut, cre] (ORCID: ), Hadley Wickham [ctb], Posit Software, PBC [cph]", + "Maintainer": "Jeroen Ooms ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "data.table": { + "Package": "data.table", + "Version": "1.18.2.1", + "Source": "Repository", + "Title": "Extension of `data.frame`", + "Depends": [ + "R (>= 3.4.0)" + ], + "Imports": [ + "methods" + ], + "Suggests": [ + "bit64 (>= 4.0.0)", + "bit (>= 4.0.4)", + "R.utils (>= 2.13.0)", + "xts", + "zoo (>= 1.8-1)", + "yaml", + "knitr", + "markdown" + ], + "Description": "Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. 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Allen [ctb, cph] (Original C implementation of Myers Diff Algorithm)", + "Maintainer": "Brodie Gaslam ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "digest": { + "Package": "digest", + "Version": "0.6.39", + "Source": "Repository", + "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Antoine\", \"Lucas\", role=\"ctb\", comment = c(ORCID = \"0000-0002-8059-9767\")), person(\"Jarek\", \"Tuszynski\", role=\"ctb\"), person(\"Henrik\", \"Bengtsson\", role=\"ctb\", comment = c(ORCID = \"0000-0002-7579-5165\")), person(\"Simon\", \"Urbanek\", role=\"ctb\", comment = c(ORCID = \"0000-0003-2297-1732\")), person(\"Mario\", \"Frasca\", role=\"ctb\"), person(\"Bryan\", \"Lewis\", role=\"ctb\"), person(\"Murray\", \"Stokely\", role=\"ctb\"), person(\"Hannes\", \"Muehleisen\", role=\"ctb\", comment = c(ORCID = \"0000-0001-8552-0029\")), person(\"Duncan\", \"Murdoch\", role=\"ctb\"), person(\"Jim\", \"Hester\", role=\"ctb\", comment = c(ORCID = \"0000-0002-2739-7082\")), person(\"Wush\", \"Wu\", role=\"ctb\", comment = c(ORCID = \"0000-0001-5180-0567\")), person(\"Qiang\", \"Kou\", role=\"ctb\", comment = c(ORCID = \"0000-0001-6786-5453\")), person(\"Thierry\", \"Onkelinx\", role=\"ctb\", comment = c(ORCID = \"0000-0001-8804-4216\")), person(\"Michel\", \"Lang\", role=\"ctb\", comment = c(ORCID = \"0000-0001-9754-0393\")), person(\"Viliam\", \"Simko\", role=\"ctb\"), person(\"Kurt\", \"Hornik\", role=\"ctb\", comment = c(ORCID = \"0000-0003-4198-9911\")), person(\"Radford\", \"Neal\", role=\"ctb\", comment = c(ORCID = \"0000-0002-2473-3407\")), person(\"Kendon\", \"Bell\", role=\"ctb\", comment = c(ORCID = \"0000-0002-9093-8312\")), person(\"Matthew\", \"de Queljoe\", role=\"ctb\"), person(\"Dmitry\", \"Selivanov\", role=\"ctb\", comment = c(ORCID = \"0000-0003-0492-6647\")), person(\"Ion\", \"Suruceanu\", role=\"ctb\", comment = c(ORCID = \"0009-0005-6446-4909\")), person(\"Bill\", \"Denney\", role=\"ctb\", comment = c(ORCID = \"0000-0002-5759-428X\")), person(\"Dirk\", \"Schumacher\", role=\"ctb\"), person(\"András\", \"Svraka\", role=\"ctb\", comment = c(ORCID = \"0009-0008-8480-1329\")), person(\"Sergey\", \"Fedorov\", role=\"ctb\", comment = c(ORCID = \"0000-0002-5970-7233\")), person(\"Will\", \"Landau\", role=\"ctb\", comment = c(ORCID = \"0000-0003-1878-3253\")), person(\"Floris\", \"Vanderhaeghe\", role=\"ctb\", comment = c(ORCID = \"0000-0002-6378-6229\")), person(\"Kevin\", \"Tappe\", role=\"ctb\"), person(\"Harris\", \"McGehee\", role=\"ctb\"), person(\"Tim\", \"Mastny\", role=\"ctb\"), person(\"Aaron\", \"Peikert\", role=\"ctb\", comment = c(ORCID = \"0000-0001-7813-818X\")), person(\"Mark\", \"van der Loo\", role=\"ctb\", comment = c(ORCID = \"0000-0002-9807-4686\")), person(\"Chris\", \"Muir\", role=\"ctb\", comment = c(ORCID = \"0000-0003-2555-3878\")), person(\"Moritz\", \"Beller\", role=\"ctb\", comment = c(ORCID = \"0000-0003-4852-0526\")), person(\"Sebastian\", \"Campbell\", role=\"ctb\", comment = c(ORCID = \"0009-0000-5948-4503\")), person(\"Winston\", \"Chang\", role=\"ctb\", comment = c(ORCID = \"0000-0002-1576-2126\")), person(\"Dean\", \"Attali\", role=\"ctb\", comment = c(ORCID = \"0000-0002-5645-3493\")), person(\"Michael\", \"Chirico\", role=\"ctb\", comment = c(ORCID = \"0000-0003-0787-087X\")), person(\"Kevin\", \"Ushey\", role=\"ctb\", comment = c(ORCID = \"0000-0003-2880-7407\")), person(\"Carl\", \"Pearson\", role=\"ctb\", comment = c(ORCID = \"0000-0003-0701-7860\")))", + "Date": "2025-11-19", + "Title": "Create Compact Hash Digests of R Objects", + "Description": "Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128' algorithms) permitting easy comparison of R language objects, as well as functions such as 'hmac()' to create hash-based message authentication code. 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The \"v4\" refers to the fact that the Sheets API is currently at version 4. This package can read and write both the metadata and the cell data in a Sheet.", + "License": "MIT + file LICENSE", + "URL": "https://googlesheets4.tidyverse.org, https://github.com/tidyverse/googlesheets4", + "BugReports": "https://github.com/tidyverse/googlesheets4/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Imports": [ + "cellranger", + "cli (>= 3.0.0)", + "curl", + "gargle (>= 1.6.0)", + "glue (>= 1.3.0)", + "googledrive (>= 2.1.0)", + "httr", + "ids", + "lifecycle", + "magrittr", + "methods", + "purrr", + "rematch2", + "rlang (>= 1.0.2)", + "tibble (>= 2.1.1)", + "utils", + "vctrs (>= 0.2.3)", + "withr" + ], + "Suggests": [ + "readr", + "rmarkdown", + "spelling", + "testthat (>= 3.1.7)" + ], + "ByteCompile": "true", + "Config/Needs/website": "tidyverse, tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "Language": "en-US", + "RoxygenNote": "7.3.2.9000", + "NeedsCompilation": "no", + "Author": "Jennifer Bryan [cre, aut] (ORCID: ), Posit Software, PBC [cph, fnd]", + "Maintainer": "Jennifer Bryan ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "gower": { + "Package": "gower", + "Version": "1.0.2", + "Source": "Repository", + "Maintainer": "Mark van der Loo ", + "License": "GPL-3", + "Title": "Gower's Distance", + "Type": "Package", + "LazyLoad": "yes", + "Authors@R": "c( person(\"Mark\", \"van der Loo\", role=c(\"aut\",\"cre\"),email=\"mark.vanderloo@gmail.com\") , person(\"David\", \"Turner\", role=\"ctb\"))", + "Description": "Compute Gower's distance (or similarity) coefficient between records. 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With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which 'gt' handles things for you means that you don't often have to worry about the fine details.", + "License": "MIT + file LICENSE", + "URL": "https://gt.rstudio.com, https://github.com/rstudio/gt", + "BugReports": "https://github.com/rstudio/gt/issues", + "Depends": [ + "R (>= 4.1.0)" + ], + "Imports": [ + "base64enc (>= 0.1-3)", + "bigD (>= 0.2)", + "bitops (>= 1.0-7)", + "cli (>= 3.6.3)", + "commonmark (>= 1.9.1)", + "dplyr (>= 1.1.4)", + "fs (>= 1.6.4)", + "glue (>= 1.8.0)", + "htmltools (>= 0.5.8.1)", + "htmlwidgets (>= 1.6.4)", + "juicyjuice (>= 0.1.0)", + "magrittr (>= 2.0.3)", + "markdown (>= 1.13)", + "reactable (>= 0.4.4)", + "rlang (>= 1.1.4)", + "sass (>= 0.4.9)", + "scales (>= 1.3.0)", + "tidyselect (>= 1.2.1)", + "vctrs", + "xml2 (>= 1.3.6)" + ], + "Suggests": [ + "bit64", + "farver", + "fontawesome (>= 0.5.2)", + "ggplot2", + "grid", + "gtable (>= 0.3.6)", + "katex (>= 1.4.1)", + "knitr", + "lubridate", + "magick", + "paletteer", + "RColorBrewer", + "rmarkdown (>= 2.20)", + "rsvg", + "rvest", + "shiny (>= 1.9.1)", + "testthat (>= 3.1.9)", + "tidyr (>= 1.0.0)", + "webshot2 (>= 0.1.0)", + "withr" + ], + "Config/Needs/coverage": "officer", + "Config/Needs/website": "quarto", + "ByteCompile": "true", + "Config/testthat/edition": "3", + "Config/testthat/parallel": "true", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "Richard Iannone [aut, cre] (ORCID: ), Joe Cheng [aut], Barret Schloerke [aut] (ORCID: ), Shannon Haughton [aut], Ellis Hughes [aut] (ORCID: ), Alexandra Lauer [aut] (ORCID: ), Romain François [aut], JooYoung Seo [aut] (ORCID: ), Ken Brevoort [aut] (ORCID: ), Olivier Roy [aut], Posit Software, PBC [cph, fnd]", + "Maintainer": "Richard Iannone ", + "Repository": "P3M" + }, + "gtable": { + "Package": "gtable", + "Version": "0.3.6", + "Source": "Repository", + "Title": "Arrange 'Grobs' in Tables", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Tools to make it easier to work with \"tables\" of 'grobs'. The 'gtable' package defines a 'gtable' grob class that specifies a grid along with a list of grobs and their placement in the grid. Further the package makes it easy to manipulate and combine 'gtable' objects so that complex compositions can be built up sequentially.", + "License": "MIT + file LICENSE", + "URL": "https://gtable.r-lib.org, https://github.com/r-lib/gtable", + "BugReports": "https://github.com/r-lib/gtable/issues", + "Depends": [ + "R (>= 4.0)" + ], + "Imports": [ + "cli", + "glue", + "grid", + "lifecycle", + "rlang (>= 1.1.0)", + "stats" + ], + "Suggests": [ + "covr", + "ggplot2", + "knitr", + "profvis", + "rmarkdown", + "testthat (>= 3.0.0)" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2024-10-25", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Hadley Wickham [aut], Thomas Lin Pedersen [aut, cre], Posit Software, PBC [cph, fnd]", + "Maintainer": "Thomas Lin Pedersen ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "gtools": { + "Package": "gtools", + "Version": "3.9.5", + "Source": "Repository", + "Title": "Various R Programming Tools", + "Description": "Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords').", + "Authors@R": "c(person(\"Gregory R.\", \"Warnes\", role = \"aut\"), person(\"Ben\", \"Bolker\", role = c(\"aut\", \"cre\"), email = \"bolker@mcmaster.ca\", comment=c(ORCID=\"0000-0002-2127-0443\")), person(\"Thomas\", \"Lumley\", role = \"aut\"), person(\"Arni\", \"Magnusson\", role = \"aut\"), person(\"Bill\", \"Venables\", role = \"aut\"), person(\"Genei\", \"Ryodan\", role = \"aut\"), person(\"Steffen\", \"Moeller\", role = \"aut\"), person(\"Ian\", \"Wilson\", role = \"ctb\"), person(\"Mark\", \"Davis\", role = \"ctb\"), person(\"Nitin\", \"Jain\", role=\"ctb\"), person(\"Scott\", \"Chamberlain\", role = \"ctb\"))", + "License": "GPL-2", + "Depends": [ + "methods", + "stats", + "utils" + ], + "URL": "https://github.com/r-gregmisc/gtools", + "BugReports": "https://github.com/r-gregmisc/gtools/issues", + "Language": "en-US", + "Suggests": [ + "car", + "gplots", + "knitr", + "rstudioapi", + "SGP", + "taxize" + ], + "RoxygenNote": "7.2.3", + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Author": "Gregory R. Warnes [aut], Ben Bolker [aut, cre] (), Thomas Lumley [aut], Arni Magnusson [aut], Bill Venables [aut], Genei Ryodan [aut], Steffen Moeller [aut], Ian Wilson [ctb], Mark Davis [ctb], Nitin Jain [ctb], Scott Chamberlain [ctb]", + "Maintainer": "Ben Bolker ", + "Repository": "P3M" + }, + "hardhat": { + "Package": "hardhat", + "Version": "1.4.2", + "Source": "Repository", + "Title": "Construct Modeling Packages", + "Authors@R": "c( person(\"Hannah\", \"Frick\", , \"hannah@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-6049-5258\")), person(\"Davis\", \"Vaughan\", , \"davis@posit.co\", role = \"aut\"), person(\"Max\", \"Kuhn\", , \"max@posit.co\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/tidymodels/hardhat, https://hardhat.tidymodels.org", + "BugReports": "https://github.com/tidymodels/hardhat/issues", + "Depends": [ + "R (>= 4.1)" + ], + "Imports": [ + "cli (>= 3.6.0)", + "glue (>= 1.6.2)", + "rlang (>= 1.1.0)", + "sparsevctrs (>= 0.2.0)", + "tibble (>= 3.2.1)", + "vctrs (>= 0.6.0)" + ], + "Suggests": [ + "covr", + "crayon", + "devtools", + "knitr", + "Matrix", + "modeldata (>= 0.0.2)", + "recipes (>= 1.0.5)", + "rmarkdown (>= 2.3)", + "roxygen2", + "testthat (>= 3.0.0)", + "usethis (>= 2.1.5)", + "withr (>= 3.0.0)" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-23", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Hannah Frick [aut, cre] (ORCID: ), Davis Vaughan [aut], Max Kuhn [aut], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Hannah Frick ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "haven": { + "Package": "haven", + "Version": "2.5.5", + "Source": "Repository", + "Title": "Import and Export 'SPSS', 'Stata' and 'SAS' Files", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\")), person(\"Evan\", \"Miller\", role = c(\"aut\", \"cph\"), comment = \"Author of included ReadStat code\"), person(\"Danny\", \"Smith\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Import foreign statistical formats into R via the embedded 'ReadStat' C library, .", + "License": "MIT + file LICENSE", + "URL": "https://haven.tidyverse.org, https://github.com/tidyverse/haven, https://github.com/WizardMac/ReadStat", + "BugReports": "https://github.com/tidyverse/haven/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Imports": [ + "cli (>= 3.0.0)", + "forcats (>= 0.2.0)", + "hms", + "lifecycle", + "methods", + "readr (>= 0.1.0)", + "rlang (>= 0.4.0)", + "tibble", + "tidyselect", + "vctrs (>= 0.3.0)" + ], + "Suggests": [ + "covr", + "crayon", + "fs", + "knitr", + "pillar (>= 1.4.0)", + "rmarkdown", + "testthat (>= 3.0.0)", + "utf8" + ], + "LinkingTo": [ + "cpp11" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "SystemRequirements": "GNU make, zlib: zlib1g-dev (deb), zlib-devel (rpm)", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut, cre], Evan Miller [aut, cph] (Author of included ReadStat code), Danny Smith [aut], Posit Software, PBC [cph, fnd]", + "Maintainer": "Hadley Wickham ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "here": { + "Package": "here", + "Version": "1.0.2", + "Source": "Repository", + "Title": "A Simpler Way to Find Your Files", + "Date": "2025-09-06", + "Authors@R": "c(person(given = \"Kirill\", family = \"M\\u00fcller\", role = c(\"aut\", \"cre\"), email = \"kirill@cynkra.com\", comment = c(ORCID = \"0000-0002-1416-3412\")), person(given = \"Jennifer\", family = \"Bryan\", role = \"ctb\", email = \"jenny@rstudio.com\", comment = c(ORCID = \"0000-0002-6983-2759\")))", + "Description": "Constructs paths to your project's files. Declare the relative path of a file within your project with 'i_am()'. Use the 'here()' function as a drop-in replacement for 'file.path()', it will always locate the files relative to your project root.", + "License": "MIT + file LICENSE", + "URL": "https://here.r-lib.org/, https://github.com/r-lib/here", + "BugReports": "https://github.com/r-lib/here/issues", + "Imports": [ + "rprojroot (>= 2.1.0)" + ], + "Suggests": [ + "conflicted", + "covr", + "fs", + "knitr", + "palmerpenguins", + "plyr", + "readr", + "rlang", + "rmarkdown", + "testthat", + "uuid", + "withr" + ], + "VignetteBuilder": "knitr", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3.9000", + "Config/testthat/edition": "3", + "Config/Needs/website": "tidyverse/tidytemplate", + "NeedsCompilation": "no", + "Author": "Kirill Müller [aut, cre] (ORCID: ), Jennifer Bryan [ctb] (ORCID: )", + "Maintainer": "Kirill Müller ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "highr": { + "Package": "highr", + "Version": "0.11", + "Source": "Repository", + "Type": "Package", + "Title": "Syntax Highlighting for R Source Code", + "Authors@R": "c( person(\"Yihui\", \"Xie\", role = c(\"aut\", \"cre\"), email = \"xie@yihui.name\", comment = c(ORCID = \"0000-0003-0645-5666\")), person(\"Yixuan\", \"Qiu\", role = \"aut\"), person(\"Christopher\", \"Gandrud\", role = \"ctb\"), person(\"Qiang\", \"Li\", role = \"ctb\") )", + "Description": "Provides syntax highlighting for R source code. Currently it supports LaTeX and HTML output. 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PBC\", role = \"fnd\", comment = c(ROR = \"03wc8by49\")) )", + "Description": "Implements an S3 class for storing and formatting time-of-day values, based on the 'difftime' class.", + "License": "MIT + file LICENSE", + "URL": "https://hms.tidyverse.org/, https://github.com/tidyverse/hms", + "BugReports": "https://github.com/tidyverse/hms/issues", + "Imports": [ + "cli", + "lifecycle", + "methods", + "pkgconfig", + "rlang (>= 1.0.2)", + "vctrs (>= 0.3.8)" + ], + "Suggests": [ + "crayon", + "lubridate", + "pillar (>= 1.1.0)", + "testthat (>= 3.0.0)" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3.9000", + "NeedsCompilation": "no", + "Author": "Kirill Müller [aut, cre] (ORCID: ), R Consortium [fnd], Posit Software, PBC [fnd] (ROR: )", + "Maintainer": "Kirill Müller ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "htmltools": { + "Package": "htmltools", + "Version": "0.5.9", + 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within R. It is primarily intended as a building block for other packages, rather than making it particularly easy to create complete web applications using httpuv alone. httpuv is built on top of the libuv and http-parser C libraries, both of which were developed by Joyent, Inc. (See LICENSE file for libuv and http-parser license information.)", + "License": "GPL (>= 2) | file LICENSE", + "URL": "https://github.com/rstudio/httpuv", + "BugReports": "https://github.com/rstudio/httpuv/issues", + "Depends": [ + "R (>= 2.15.1)" + ], + "Imports": [ + "later (>= 0.8.0)", + "promises", + "R6", + "Rcpp (>= 1.0.7)", + "utils" + ], + "Suggests": [ + "callr", + "curl", + "jsonlite", + "testthat", + "websocket" + ], + "LinkingTo": [ + "later", + "Rcpp" + ], + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "SystemRequirements": "GNU make, zlib", + "Collate": "'RcppExports.R' 'httpuv.R' 'random_port.R' 'server.R' 'staticServer.R' 'static_paths.R' 'utils.R'", + "NeedsCompilation": "yes", + "Author": "Joe Cheng [aut], Winston Chang [aut, cre], Posit, PBC fnd [cph], Hector Corrada Bravo [ctb], Jeroen Ooms [ctb], Andrzej Krzemienski [cph] (optional.hpp), libuv project contributors [cph] (libuv library, see src/libuv/AUTHORS file), Joyent, Inc. and other Node contributors [cph] (libuv library, see src/libuv/AUTHORS file; and http-parser library, see src/http-parser/AUTHORS file), Niels Provos [cph] (libuv subcomponent: tree.h), Internet Systems Consortium, Inc. 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Configuration functions make it easy to control additional request components (authenticate(), add_headers() and so on).", + "License": "MIT + file LICENSE", + "URL": "https://httr.r-lib.org/, https://github.com/r-lib/httr", + "BugReports": "https://github.com/r-lib/httr/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Imports": [ + "curl (>= 5.1.0)", + "jsonlite", + "mime", + "openssl (>= 0.8)", + "R6" + ], + "Suggests": [ + "covr", + "httpuv", + "jpeg", + "knitr", + "png", + "readr", + "rmarkdown", + "testthat (>= 0.8.0)", + "xml2" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd]", + "Maintainer": "Hadley Wickham ", + "Repository": "CRAN" + }, + "httr2": { + "Package": "httr2", + "Version": "1.2.2", + "Source": "Repository", + "Title": "Perform HTTP Requests and Process the Responses", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"Maximilian\", \"Girlich\", role = \"ctb\") )", + "Description": "Tools for creating and modifying HTTP requests, then performing them and processing the results. 'httr2' is a modern re-imagining of 'httr' that uses a pipe-based interface and solves more of the problems that API wrapping packages face.", + "License": "MIT + file LICENSE", + "URL": "https://httr2.r-lib.org, https://github.com/r-lib/httr2", + "BugReports": "https://github.com/r-lib/httr2/issues", + "Depends": [ + "R (>= 4.1)" + ], + "Imports": [ + "cli (>= 3.0.0)", + "curl (>= 6.4.0)", + "glue", + "lifecycle", + "magrittr", + "openssl", + "R6", + "rappdirs", + "rlang (>= 1.1.0)", + "vctrs (>= 0.6.3)", + "withr" + ], + "Suggests": [ + "askpass", + "bench", + "clipr", + "covr", + "docopt", + "httpuv", + "jose", + "jsonlite", + "knitr", + "later (>= 1.4.0)", + "nanonext", + "otel (>= 0.2.0)", + "otelsdk (>= 0.2.0)", + "paws.common (>= 0.8.0)", + "promises", + "rmarkdown", + "testthat (>= 3.1.8)", + "tibble", + "webfakes (>= 1.4.0)", + "xml2" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/testthat/parallel": "true", + "Config/testthat/start-first": "resp-stream, req-perform", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd], Maximilian Girlich [ctb]", + "Maintainer": "Hadley Wickham ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "ids": { + "Package": "ids", + "Version": "1.0.1", + "Source": "Repository", + "Title": "Generate Random Identifiers", + "Authors@R": "person(\"Rich\", \"FitzJohn\", role = c(\"aut\", \"cre\"), email = \"rich.fitzjohn@gmail.com\")", + "Description": "Generate random or human readable and pronounceable identifiers.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/richfitz/ids", + "BugReports": "https://github.com/richfitz/ids/issues", + "Imports": [ + "openssl", + "uuid" + ], + "Suggests": [ + "knitr", + "rcorpora", + "rmarkdown", + "testthat" + ], + "RoxygenNote": "6.0.1", + "VignetteBuilder": "knitr", + "NeedsCompilation": "no", + "Author": "Rich FitzJohn [aut, cre]", + "Maintainer": "Rich FitzJohn ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest", + "Encoding": "UTF-8" + }, + "igraph": { + "Package": "igraph", + "Version": "2.2.2", + "Source": "Repository", + "Title": "Network Analysis and Visualization", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0001-7098-9676\")), person(\"Tamás\", \"Nepusz\", , \"ntamas@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-1451-338X\")), person(\"Vincent\", \"Traag\", role = \"aut\", comment = c(ORCID = \"0000-0003-3170-3879\")), person(\"Szabolcs\", \"Horvát\", , \"szhorvat@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-3100-523X\")), person(\"Fabio\", \"Zanini\", , \"fabio.zanini@unsw.edu.au\", role = \"aut\", comment = c(ORCID = \"0000-0001-7097-8539\")), person(\"Daniel\", \"Noom\", role = \"aut\"), person(\"Kirill\", \"Müller\", , \"kirill@cynkra.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-1416-3412\")), person(\"Michael\", \"Antonov\", role = \"ctb\"), person(\"Chan Zuckerberg Initiative\", role = \"fnd\", comment = c(ROR = \"02qenvm24\")), person(\"David\", \"Schoch\", , \"david.schoch@cynkra.com\", role = \"aut\", comment = c(ORCID = \"0000-0003-2952-4812\")), person(\"Maëlle\", \"Salmon\", , \"maelle@cynkra.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-2815-0399\")) )", + "Description": "Routines for simple graphs and network analysis. 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[aut] (ORCID: )", + "Maintainer": "Kirill Müller ", + "Repository": "P3M" + }, + "infer": { + "Package": "infer", + "Version": "1.1.0", + "Source": "Repository", + "Type": "Package", + "Title": "Tidy Statistical Inference", + "Authors@R": "c( person(\"Andrew\", \"Bray\", , \"abray@reed.edu\", role = \"aut\"), person(\"Chester\", \"Ismay\", , \"chester.ismay@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0003-2820-2547\")), person(\"Evgeni\", \"Chasnovski\", , \"evgeni.chasnovski@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-1617-4019\")), person(\"Simon\", \"Couch\", , \"simon.couch@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-5676-5107\")), person(\"Ben\", \"Baumer\", , \"ben.baumer@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-3279-0516\")), person(\"Mine\", \"Cetinkaya-Rundel\", , \"mine@stat.duke.edu\", role = \"aut\", comment = c(ORCID = \"0000-0001-6452-2420\")), person(\"Ted\", \"Laderas\", , 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Kim [ctb] (ORCID: ), Neal Fultz [ctb], Doug Friedman [ctb], Richie Cotton [ctb] (ORCID: ), Brian Fannin [ctb]", + "Maintainer": "Simon Couch ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "ini": { + "Package": "ini", + "Version": "0.3.1", + "Source": "Repository", + "Type": "Package", + "Title": "Read and Write '.ini' Files", + "Date": "2018-05-19", + "Author": "David Valentim Dias", + "Maintainer": "David Valentim Dias ", + "Description": "Parse simple '.ini' configuration files to an structured list. Users can manipulate this resulting list with lapply() functions. 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Ripley [ctb], Terry Therneau [ctb], Beth Atkinson [ctb]", + "Maintainer": "Torsten Hothorn ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest", + "Encoding": "UTF-8" + }, + "isoband": { + "Package": "isoband", + "Version": "0.3.0", + "Source": "Repository", + "Title": "Generate Isolines and Isobands from Regularly Spaced Elevation Grids", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\", comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Claus O.\", \"Wilke\", , \"wilke@austin.utexas.edu\", role = \"aut\", comment = c(\"Original author\", ORCID = \"0000-0002-7470-9261\")), person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Posit, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.", + "License": "MIT + file LICENSE", + "URL": "https://isoband.r-lib.org, https://github.com/r-lib/isoband", + "BugReports": "https://github.com/r-lib/isoband/issues", + "Imports": [ + "cli", + "grid", + "rlang", + "utils" + ], + "Suggests": [ + "covr", + "ggplot2", + "knitr", + "magick", + "bench", + "rmarkdown", + "sf", + "testthat (>= 3.0.0)", + "xml2" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-12-05", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "Config/build/compilation-database": "true", + "LinkingTo": [ + "cpp11" + ], + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut] (ORCID: ), Claus O. Wilke [aut] (Original author, ORCID: ), Thomas Lin Pedersen [aut, cre] (ORCID: ), Posit, PBC [cph, fnd] (ROR: )", + "Maintainer": "Thomas Lin Pedersen ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "iterators": { + "Package": "iterators", + "Version": "1.0.14", + "Source": "Repository", + "Type": "Package", + "Title": "Provides Iterator Construct", + "Authors@R": "c(person(\"Folashade\", \"Daniel\", role=\"cre\", email=\"fdaniel@microsoft.com\"), person(\"Revolution\", \"Analytics\", role=c(\"aut\", \"cph\")), person(\"Steve\", \"Weston\", role=\"aut\"))", + "Description": "Support for iterators, which allow a programmer to traverse through all the elements of a vector, list, or other collection of data.", + "URL": "https://github.com/RevolutionAnalytics/iterators", + "Depends": [ + "R (>= 2.5.0)", + "utils" + ], + "Suggests": [ + "RUnit", + "foreach" + ], + "License": "Apache License (== 2.0)", + "NeedsCompilation": "no", + "Author": "Folashade Daniel [cre], Revolution Analytics [aut, cph], Steve Weston [aut]", + "Maintainer": "Folashade Daniel ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest", + "Encoding": "UTF-8" + }, + "jquerylib": { + "Package": "jquerylib", + "Version": "0.1.4", + "Source": "Repository", + "Title": "Obtain 'jQuery' as an HTML Dependency Object", + "Authors@R": "c( person(\"Carson\", \"Sievert\", role = c(\"aut\", \"cre\"), email = \"carson@rstudio.com\", comment = c(ORCID = \"0000-0002-4958-2844\")), person(\"Joe\", \"Cheng\", role = \"aut\", email = \"joe@rstudio.com\"), person(family = \"RStudio\", role = \"cph\"), person(family = \"jQuery Foundation\", role = \"cph\", comment = \"jQuery library and jQuery UI library\"), person(family = \"jQuery contributors\", role = c(\"ctb\", \"cph\"), comment = \"jQuery library; authors listed in inst/lib/jquery-AUTHORS.txt\") )", + "Description": "Obtain any major version of 'jQuery' () and use it in any webpage generated by 'htmltools' (e.g. 'shiny', 'htmlwidgets', and 'rmarkdown'). Most R users don't need to use this package directly, but other R packages (e.g. 'shiny', 'rmarkdown', etc.) depend on this package to avoid bundling redundant copies of 'jQuery'.", + "License": "MIT + file LICENSE", + "Encoding": "UTF-8", + "Config/testthat/edition": "3", + "RoxygenNote": "7.0.2", + "Imports": [ + "htmltools" + ], + "Suggests": [ + "testthat" + ], + "NeedsCompilation": "no", + "Author": "Carson Sievert [aut, cre] (), Joe Cheng [aut], RStudio [cph], jQuery Foundation [cph] (jQuery library and jQuery UI library), jQuery contributors [ctb, cph] (jQuery library; authors listed in inst/lib/jquery-AUTHORS.txt)", + "Maintainer": "Carson Sievert ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "jsonlite": { + "Package": "jsonlite", + "Version": "2.0.0", + "Source": "Repository", + "Title": "A Simple and Robust JSON Parser and Generator for R", + "License": "MIT + file LICENSE", + "Depends": [ + "methods" + ], + "Authors@R": "c( person(\"Jeroen\", \"Ooms\", role = c(\"aut\", \"cre\"), email = \"jeroenooms@gmail.com\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"Duncan\", \"Temple Lang\", role = \"ctb\"), person(\"Lloyd\", \"Hilaiel\", role = \"cph\", comment=\"author of bundled libyajl\"))", + "URL": "https://jeroen.r-universe.dev/jsonlite https://arxiv.org/abs/1403.2805", + "BugReports": "https://github.com/jeroen/jsonlite/issues", + "Maintainer": "Jeroen Ooms ", + "VignetteBuilder": "knitr, R.rsp", + "Description": "A reasonably fast JSON parser and generator, optimized for statistical data and the web. Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.", + "Suggests": [ + "httr", + "vctrs", + "testthat", + "knitr", + "rmarkdown", + "R.rsp", + "sf" + ], + "RoxygenNote": "7.3.2", + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Author": "Jeroen Ooms [aut, cre] (), Duncan Temple Lang [ctb], Lloyd Hilaiel [cph] (author of bundled libyajl)", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "juicyjuice": { + "Package": "juicyjuice", + "Version": "0.1.0", + "Source": "Repository", + "Title": "Inline CSS Properties into HTML Tags Using 'juice'", + "Authors@R": "c( person(\"Richard\", \"Iannone\", , \"riannone@me.com\", c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0003-3925-190X\")), person(\"Automattic\", role = c(\"cph\"), comment = \"juice library\"), person(\"juice contributors\", role = c(\"ctb\"), comment = \"juice library\") )", + "Description": "There are occasions where you need a piece of HTML with integrated styles. A prime example of this is HTML email. This transformation involves moving the CSS and associated formatting instructions from the style block in the head of your document into the body of the HTML. Many prominent email clients require integrated styles in HTML email; otherwise a received HTML email will be displayed without any styling. This package will quickly and precisely perform these CSS transformations when given HTML text and it does so by using the JavaScript 'juice' library.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/rich-iannone/juicyjuice", + "BugReports": "https://github.com/rich-iannone/juicyjuice/issues", + "Encoding": "UTF-8", + "RoxygenNote": "7.2.1", + "Imports": [ + "V8 (>= 4.2.0)" + ], + "Suggests": [ + "testthat (>= 3.0.0)" + ], + "Config/testthat/edition": "3", + "NeedsCompilation": "no", + "Author": "Richard Iannone [aut, cre, cph] (), Automattic [cph] (juice library), juice contributors [ctb] (juice library)", + "Maintainer": "Richard Iannone ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "knitr": { + "Package": "knitr", + "Version": "1.51", + "Source": "Repository", + "Type": "Package", + "Title": "A General-Purpose Package for Dynamic Report Generation in R", + "Authors@R": "c( person(\"Yihui\", \"Xie\", role = c(\"aut\", \"cre\"), email = \"xie@yihui.name\", comment = c(ORCID = \"0000-0003-0645-5666\", URL = \"https://yihui.org\")), person(\"Abhraneel\", \"Sarma\", role = \"ctb\"), person(\"Adam\", \"Vogt\", role = \"ctb\"), person(\"Alastair\", \"Andrew\", role = \"ctb\"), person(\"Alex\", \"Zvoleff\", role = \"ctb\"), person(\"Amar\", \"Al-Zubaidi\", role = \"ctb\"), person(\"Andre\", \"Simon\", role = \"ctb\", comment = \"the CSS files under inst/themes/ were derived from the Highlight package http://www.andre-simon.de\"), person(\"Aron\", \"Atkins\", role = \"ctb\"), person(\"Aaron\", \"Wolen\", role = \"ctb\"), person(\"Ashley\", \"Manton\", role = \"ctb\"), person(\"Atsushi\", \"Yasumoto\", role = \"ctb\", comment = c(ORCID = \"0000-0002-8335-495X\")), person(\"Ben\", \"Baumer\", role = \"ctb\"), person(\"Brian\", \"Diggs\", role = \"ctb\"), person(\"Brian\", \"Zhang\", role = \"ctb\"), person(\"Bulat\", \"Yapparov\", role = \"ctb\"), person(\"Cassio\", \"Pereira\", role = \"ctb\"), person(\"Christophe\", 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They work in terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the 'macOS' 'R.app'. The package also provides a 'C++' 'API', that works with or without 'Rcpp'.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/progress#readme, http://r-lib.github.io/progress/", + "BugReports": "https://github.com/r-lib/progress/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Imports": [ + "crayon", + "hms", + "prettyunits", + "R6" + ], + "Suggests": [ + "Rcpp", + "testthat (>= 3.0.0)", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.2.3", + "NeedsCompilation": "no", + "Author": "Gábor Csárdi [aut, cre], Rich FitzJohn [aut], Posit Software, PBC [cph, fnd]", + "Maintainer": "Gábor Csárdi ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "progressr": { + "Package": "progressr", + "Version": "0.18.0", + "Source": "Repository", + "Title": "An Inclusive, Unifying API for Progress Updates", + "Description": "A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. 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It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.", + "Authors@R": "c(person(\"Henrik\", \"Bengtsson\", role = c(\"aut\", \"cre\", \"cph\"), email = \"henrikb@braju.com\", comment = c(ORCID = \"0000-0002-7579-5165\")))", + "License": "GPL (>= 3)", + "Depends": [ + "R (>= 3.5.0)" + ], + "Imports": [ + "digest", + "utils" + ], + "Suggests": [ + "graphics", + "tcltk", + "beepr", + "cli", + "crayon", + "pbmcapply", + "progress", + "purrr", + "foreach", + "plyr", + "doFuture", + "future", + "future.apply", + "furrr", + "ntfy", + "RPushbullet", + "rstudioapi", + "shiny", + "commonmark", + "base64enc", + "tools" + ], + "VignetteBuilder": "progressr", + "Language": "en-US", + "Encoding": "UTF-8", + "URL": "https://progressr.futureverse.org, https://github.com/futureverse/progressr", + "BugReports": "https://github.com/futureverse/progressr/issues", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "Henrik Bengtsson [aut, cre, cph] (ORCID: )", + "Maintainer": "Henrik Bengtsson ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "promises": { + "Package": "promises", + "Version": "1.5.0", + "Source": "Repository", + "Type": "Package", + "Title": "Abstractions for Promise-Based Asynchronous Programming", + "Authors@R": "c( person(\"Joe\", \"Cheng\", , \"joe@posit.co\", role = \"aut\"), person(\"Barret\", \"Schloerke\", , \"barret@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-9986-114X\")), person(\"Winston\", \"Chang\", , \"winston@posit.co\", role = \"aut\", comment = c(ORCID = \"0000-0002-1576-2126\")), person(\"Charlie\", \"Gao\", , \"charlie.gao@posit.co\", role = \"aut\", comment = c(ORCID = \"0000-0002-0750-061X\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "Provides fundamental abstractions for doing asynchronous programming in R using promises. Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to 'JavaScript' promises, but with a syntax that is idiomatic R.", + "License": "MIT + file LICENSE", + "URL": "https://rstudio.github.io/promises/, https://github.com/rstudio/promises", + "BugReports": "https://github.com/rstudio/promises/issues", + "Depends": [ + "R (>= 4.1.0)" + ], + "Imports": [ + "fastmap (>= 1.1.0)", + "later", + "lifecycle", + "magrittr (>= 1.5)", + "otel (>= 0.2.0)", + "R6", + "rlang" + ], + "Suggests": [ + "future (>= 1.21.0)", + "knitr", + "mirai", + "otelsdk (>= 0.2.0)", + "purrr", + "Rcpp", + "rmarkdown", + "spelling", + "testthat (>= 3.0.0)", + "vembedr" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "rsconnect, tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-05-27", + "Encoding": "UTF-8", + "Language": "en-US", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "Joe Cheng [aut], Barret Schloerke [aut, cre] (ORCID: ), Winston Chang [aut] (ORCID: ), Charlie Gao [aut] (ORCID: ), Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Barret Schloerke ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "ps": { + "Package": "ps", + "Version": "1.9.1", + "Source": "Repository", + "Title": "List, Query, Manipulate System Processes", + "Authors@R": "c( person(\"Jay\", \"Loden\", role = \"aut\"), person(\"Dave\", \"Daeschler\", role = \"aut\"), person(\"Giampaolo\", \"Rodola'\", role = \"aut\"), person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "List, query and manipulate all system processes, on 'Windows', 'Linux' and 'macOS'.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/ps, https://ps.r-lib.org/", + "BugReports": "https://github.com/r-lib/ps/issues", + "Depends": [ + "R (>= 3.4)" + ], + "Imports": [ + "utils" + ], + "Suggests": [ + "callr", + "covr", + "curl", + "pillar", + "pingr", + "processx (>= 3.1.0)", + "R6", + "rlang", + "testthat (>= 3.0.0)", + "webfakes", + "withr" + ], + "Biarch": "true", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "yes", + "Author": "Jay Loden [aut], Dave Daeschler [aut], Giampaolo Rodola' [aut], Gábor Csárdi [aut, cre], Posit Software, PBC [cph, fnd]", + "Maintainer": "Gábor Csárdi ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "purrr": { + "Package": "purrr", + "Version": "1.2.1", + "Source": "Repository", + "Title": "Functional Programming Tools", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Lionel\", \"Henry\", , \"lionel@posit.co\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"https://ror.org/03wc8by49\")) )", + "Description": "A complete and consistent functional programming toolkit for R.", + "License": "MIT + file LICENSE", + "URL": "https://purrr.tidyverse.org/, https://github.com/tidyverse/purrr", + "BugReports": "https://github.com/tidyverse/purrr/issues", + "Depends": [ + "R (>= 4.1)" + ], + "Imports": [ + "cli (>= 3.6.1)", + "lifecycle (>= 1.0.3)", + "magrittr (>= 1.5.0)", + "rlang (>= 1.1.1)", + "vctrs (>= 0.6.3)" + ], + "Suggests": [ + "carrier (>= 0.3.0)", + "covr", + "dplyr (>= 0.7.8)", + "httr", + "knitr", + "lubridate", + "mirai (>= 2.5.1)", + "rmarkdown", + "testthat (>= 3.0.0)", + "tibble", + "tidyselect" + ], + "LinkingTo": [ + "cli" + ], + "VignetteBuilder": "knitr", + "Biarch": "true", + "Config/build/compilation-database": "true", + "Config/Needs/website": "tidyverse/tidytemplate, tidyr", + "Config/testthat/edition": "3", + "Config/testthat/parallel": "TRUE", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut, cre] (ORCID: ), Lionel Henry [aut], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Hadley Wickham ", + "Repository": "P3M" + }, + "qap": { + "Package": "qap", + "Version": "0.1-2", + "Source": "Repository", + "Title": "Heuristics for the Quadratic Assignment Problem (QAP)", + "Date": "2022-06-27", + "Authors@R": "c(person(\"Michael\", \"Hahsler\", role = c(\"aut\", \"cre\", \"cph\"), email = \"mhahsler@lyle.smu.edu\", comment = c(ORCID = \"0000-0003-2716-1405\")), person(\"Franz\", \"Rendl\", role = c(\"ctb\", \"cph\")))", + "Description": "Implements heuristics for the Quadratic Assignment Problem (QAP). Although, the QAP was introduced as a combinatorial optimization problem for the facility location problem in operations research, it also has many applications in data analysis. The problem is NP-hard and the package implements a simulated annealing heuristic.", + "Suggests": [ + "testthat" + ], + "URL": "https://github.com/mhahsler/qap", + "BugReports": "https://github.com/mhahsler/qap/issues", + "License": "GPL-3", + "NeedsCompilation": "yes", + "Author": "Michael Hahsler [aut, cre, cph] (), Franz Rendl [ctb, cph]", + "Maintainer": "Michael Hahsler ", + "Repository": "https://packagemanager.posit.co/cran/latest", + "Encoding": "UTF-8" + }, + "quarto": { + "Package": "quarto", + "Version": "1.5.1", + "Source": "Repository", + "Title": "R Interface to 'Quarto' Markdown Publishing System", + "Authors@R": "c( person(\"JJ\", \"Allaire\", , \"jj@posit.co\", role = \"aut\", comment = c(ORCID = \"0000-0003-0174-9868\")), person(\"Christophe\", \"Dervieux\", , \"cderv@posit.co\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0003-4474-2498\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"Gordon\", \"Woodhull\", role = \"ctb\") )", + "Description": "Convert R Markdown documents and 'Jupyter' notebooks to a variety of output formats using 'Quarto'.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/quarto-dev/quarto-r, https://quarto-dev.github.io/quarto-r/", + "BugReports": "https://github.com/quarto-dev/quarto-r/issues", + "Depends": [ + "R (>= 4.1.0)" + ], + "Imports": [ + "cli", + "fs", + "htmltools", + "jsonlite", + "later", + "lifecycle", + "processx", + "rlang", + "rmarkdown", + "rstudioapi", + "tools", + "utils", + "xfun", + "yaml (>= 2.3.10)" + ], + "Suggests": [ + "bslib", + "callr", + "curl", + "dplyr", + "flextable", + "ggiraph", + "ggplot2", + "gt", + "heatmaply", + "kableExtra", + "knitr", + "palmerpenguins", + "patchwork", + "pkgload", + "plotly", + "rsconnect (>= 0.8.26)", + "testthat (>= 3.1.7)", + "thematic", + "tidyverse", + "tinytable", + "whoami", + "withr" + ], + "VignetteBuilder": "quarto", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "SystemRequirements": "Quarto command line tool ().", + "NeedsCompilation": "no", + "Author": "JJ Allaire [aut] (ORCID: ), Christophe Dervieux [cre, aut] (ORCID: ), Posit Software, PBC [cph, fnd], Gordon Woodhull [ctb]", + "Maintainer": "Christophe Dervieux ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "ragg": { + "Package": "ragg", + "Version": "1.5.0", + "Source": "Repository", + "Type": "Package", + "Title": "Graphic Devices Based on AGG", + "Authors@R": "c( person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Maxim\", \"Shemanarev\", role = c(\"aut\", \"cph\"), comment = \"Author of AGG\"), person(\"Tony\", \"Juricic\", , \"tonygeek@yahoo.com\", role = c(\"ctb\", \"cph\"), comment = \"Contributor to AGG\"), person(\"Milan\", \"Marusinec\", , \"milan@marusinec.sk\", role = c(\"ctb\", \"cph\"), comment = \"Contributor to AGG\"), person(\"Spencer\", \"Garrett\", role = \"ctb\", comment = \"Contributor to AGG\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Maintainer": "Thomas Lin Pedersen ", + "Description": "Anti-Grain Geometry (AGG) is a high-quality and high-performance 2D drawing library. The 'ragg' package provides a set of graphic devices based on AGG to use as alternative to the raster devices provided through the 'grDevices' package.", + "License": "MIT + file LICENSE", + "URL": "https://ragg.r-lib.org, https://github.com/r-lib/ragg", + "BugReports": "https://github.com/r-lib/ragg/issues", + "Imports": [ + "systemfonts (>= 1.0.3)", + "textshaping (>= 0.3.0)" + ], + "Suggests": [ + "covr", + "graphics", + "grid", + "testthat (>= 3.0.0)" + ], + "LinkingTo": [ + "systemfonts", + "textshaping" + ], + "Config/build/compilation-database": "true", + "Config/Needs/website": "ggplot2, devoid, magick, bench, tidyr, ggridges, hexbin, sessioninfo, pkgdown, tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-25", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "SystemRequirements": "freetype2, libpng, libtiff, libjpeg, libwebp, libwebpmux", + "NeedsCompilation": "yes", + "Author": "Thomas Lin Pedersen [cre, aut] (ORCID: ), Maxim Shemanarev [aut, cph] (Author of AGG), Tony Juricic [ctb, cph] (Contributor to AGG), Milan Marusinec [ctb, cph] (Contributor to AGG), Spencer Garrett [ctb] (Contributor to AGG), Posit Software, PBC [cph, fnd] (ROR: )", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "ranger": { + "Package": "ranger", + "Version": "0.18.0", + "Source": "Repository", + "Type": "Package", + "Title": "A Fast Implementation of Random Forests", + "Date": "2026-01-15", + "Authors@R": "c( person(\"Marvin N.\", \"Wright\", email = \"cran@wrig.de\", role = c(\"aut\", \"cre\")), person(\"Stefan\", \"Wager\", role = \"ctb\"), person(\"Philipp\", \"Probst\", role = \"ctb\"))", + "Description": "A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.", + "License": "GPL-3", + "Imports": [ + "Rcpp (>= 0.11.2)", + "Matrix" + ], + "LinkingTo": [ + "Rcpp", + "RcppEigen" + ], + "Depends": [ + "R (>= 3.1)" + ], + "Suggests": [ + "survival", + "testthat" + ], + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "URL": "https://imbs-hl.github.io/ranger/, https://github.com/imbs-hl/ranger", + "BugReports": "https://github.com/imbs-hl/ranger/issues", + "NeedsCompilation": "yes", + "Author": "Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb]", + "Maintainer": "Marvin N. 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A port of Python's 'Appdirs' () to R.", + "License": "MIT + file LICENSE", + "URL": "https://rappdirs.r-lib.org, https://github.com/r-lib/rappdirs", + "BugReports": "https://github.com/r-lib/rappdirs/issues", + "Depends": [ + "R (>= 4.1)" + ], + "Suggests": [ + "covr", + "roxygen2", + "testthat (>= 3.2.0)", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-05-05", + "Copyright": "Original python appdirs module copyright (c) 2010 ActiveState Software Inc. R port copyright Hadley Wickham, Posit, PBC. See file LICENSE for details.", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [trl, cre, cph], Sridhar Ratnakumar [aut], Trent Mick [aut], ActiveState [cph] (R/appdir.r, R/cache.r, R/data.r, R/log.r translated from appdirs), Eddy Petrisor [ctb], Trevor Davis [trl, aut] (ORCID: ), Gabor Csardi [ctb], Gregory Jefferis [ctb], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Hadley Wickham ", + "Repository": "P3M" + }, + "rbibutils": { + "Package": "rbibutils", + "Version": "2.4.1", + "Source": "Repository", + "Type": "Package", + "Title": "Read 'Bibtex' Files and Convert Between Bibliography Formats", + "Authors@R": "c( person(given = c(\"Georgi\", \"N.\"), family = \"Boshnakov\", role = c(\"aut\", \"cre\"), \t email = \"georgi.boshnakov@manchester.ac.uk\", comment = c(ORCID = \"0000-0003-2839-346X\", \"R port, R code, new C code and modifications to bibutils' C code, conversion to Bibentry (R and C code)\") ), person(given = \"Chris\", family = \"Putman\", role = \"aut\", comment = \"src/*, author of the bibutils libraries, https://sourceforge.net/projects/bibutils/\"), person(given = \"Richard\", family = \"Mathar\", role = \"ctb\", comment = \"src/addsout.c\"), person(given = \"Johannes\", family = \"Wilm\", role = \"ctb\", comment = \"src/biblatexin.c, src/bltypes.c\"), person(\"R Core Team\", role = \"ctb\", comment = \"base R's bibentry and bibstyle implementation\") )", + "Description": "Read and write 'Bibtex' files. Convert between bibliography formats, including 'Bibtex', 'Biblatex', 'PubMed', 'Endnote', and 'Bibentry'. Includes a port of the 'bibutils' utilities by Chris Putnam . Supports all bibliography formats and character encodings implemented in 'bibutils'.", + "License": "GPL-2", + "URL": "https://geobosh.github.io/rbibutils/ (doc), https://CRAN.R-project.org/package=rbibutils", + "BugReports": "https://github.com/GeoBosh/rbibutils/issues", + "Depends": [ + "R (>= 2.10)" + ], + "Imports": [ + "utils", + "tools" + ], + "Suggests": [ + "testthat" + ], + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Config/Needs/memcheck": "devtools, rcmdcheck", + "Author": "Georgi N. Boshnakov [aut, cre] (ORCID: , R port, R code, new C code and modifications to bibutils' C code, conversion to Bibentry (R and C code)), Chris Putman [aut] (src/*, author of the bibutils libraries, https://sourceforge.net/projects/bibutils/), Richard Mathar [ctb] (src/addsout.c), Johannes Wilm [ctb] (src/biblatexin.c, src/bltypes.c), R Core Team [ctb] (base R's bibentry and bibstyle implementation)", + "Maintainer": "Georgi N. Boshnakov ", + "Repository": "P3M" + }, + "rcmdcheck": { + "Package": "rcmdcheck", + "Version": "1.4.0", + "Source": "Repository", + "Title": "Run 'R CMD check' from 'R' and Capture Results", + "Authors@R": "person(given = \"Gábor\", family = \"Csárdi\", role = c(\"cre\", \"aut\"), email = \"csardi.gabor@gmail.com\")", + "Description": "Run 'R CMD check' from 'R' and capture the results of the individual checks. Supports running checks in the background, timeouts, pretty printing and comparing check results.", + "License": "MIT + file LICENSE", + "URL": "https://r-lib.github.io/rcmdcheck/, https://github.com/r-Lib/rcmdcheck#readme", + "BugReports": "https://github.com/r-Lib/rcmdcheck/issues", + "Imports": [ + "callr (>= 3.1.1.9000)", + "cli (>= 3.0.0)", + "curl", + "desc (>= 1.2.0)", + "digest", + "pkgbuild", + "prettyunits", + "R6", + "rprojroot", + "sessioninfo (>= 1.1.1)", + "utils", + "withr", + "xopen" + ], + "Suggests": [ + "covr", + "knitr", + "mockery", + "processx", + "ps", + "rmarkdown", + "svglite", + "testthat", + "webfakes" + ], + "Encoding": "UTF-8", + "RoxygenNote": "7.1.2", + "Config/testthat/edition": "3", + "NeedsCompilation": "no", + "Author": "Gábor Csárdi [cre, aut]", + "Maintainer": "Gábor Csárdi ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "reactR": { + "Package": "reactR", + "Version": "0.6.1", + "Source": "Repository", + "Type": "Package", + "Title": "React Helpers", + "Date": "2024-09-14", + "Authors@R": "c( person( \"Facebook\", \"Inc\" , role = c(\"aut\", \"cph\") , comment = \"React library in lib, https://reactjs.org/; see AUTHORS for full list of contributors\" ), person( \"Michel\",\"Weststrate\", , role = c(\"aut\", \"cph\") , comment = \"mobx library in lib, https://github.com/mobxjs\" ), person( \"Kent\", \"Russell\" , role = c(\"aut\", \"cre\") , comment = \"R interface\" , email = \"kent.russell@timelyportfolio.com\" ), person( \"Alan\", \"Dipert\" , role = c(\"aut\") , comment = \"R interface\" , email = \"alan@rstudio.com\" ), person( \"Greg\", \"Lin\" , role = c(\"aut\") , comment = \"R interface\" , email = \"glin@glin.io\" ) )", + "Maintainer": "Kent Russell ", + "Description": "Make it easy to use 'React' in R with 'htmlwidget' scaffolds, helper dependency functions, an embedded 'Babel' 'transpiler', and examples.", + "URL": "https://github.com/react-R/reactR", + "BugReports": "https://github.com/react-R/reactR/issues", + "License": "MIT + file LICENSE", + "Encoding": "UTF-8", + "Imports": [ + "htmltools" + ], + "Suggests": [ + "htmlwidgets (>= 1.5.3)", + "rmarkdown", + "shiny", + "V8", + "knitr", + "usethis", + "jsonlite" + ], + "RoxygenNote": "7.3.2", + "VignetteBuilder": "knitr", + "NeedsCompilation": "no", + "Author": "Facebook Inc [aut, cph] (React library in lib, https://reactjs.org/; see AUTHORS for full list of contributors), Michel Weststrate [aut, cph] (mobx library in lib, https://github.com/mobxjs), Kent Russell [aut, cre] (R interface), Alan Dipert [aut] (R interface), Greg Lin [aut] (R interface)", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "reactable": { + "Package": "reactable", + "Version": "0.4.5", + "Source": "Repository", + "Type": "Package", + "Title": "Interactive Data Tables for R", + "Authors@R": "c( person(\"Greg\", \"Lin\", email = \"glin@glin.io\", role = c(\"aut\", \"cre\")), person(\"Tanner\", \"Linsley\", role = c(\"ctb\", \"cph\"), comment = \"React Table library\"), person(family = \"Emotion team and other contributors\", role = c(\"ctb\", \"cph\"), comment = \"Emotion library\"), person(\"Kent\", \"Russell\", role = c(\"ctb\", \"cph\"), comment = \"reactR package\"), person(\"Ramnath\", \"Vaidyanathan\", role = c(\"ctb\", \"cph\"), comment = \"htmlwidgets package\"), person(\"Joe\", \"Cheng\", role = c(\"ctb\", \"cph\"), comment = \"htmlwidgets package\"), person(\"JJ\", \"Allaire\", role = c(\"ctb\", \"cph\"), comment = \"htmlwidgets package\"), person(\"Yihui\", \"Xie\", role = c(\"ctb\", \"cph\"), comment = \"htmlwidgets package\"), person(\"Kenton\", \"Russell\", role = c(\"ctb\", \"cph\"), comment = \"htmlwidgets package\"), person(family = \"Facebook, Inc. and its affiliates\", role = c(\"ctb\", \"cph\"), comment = \"React library\"), person(family = \"FormatJS\", role = c(\"ctb\", \"cph\"), comment = \"FormatJS libraries\"), person(family = \"Feross Aboukhadijeh, and other contributors\", role = c(\"ctb\", \"cph\"), comment = \"buffer library\"), person(\"Roman\", \"Shtylman\", role = c(\"ctb\", \"cph\"), comment = \"process library\"), person(\"James\", \"Halliday\", role = c(\"ctb\", \"cph\"), comment = \"stream-browserify library\"), person(family = \"Posit Software, PBC\", role = c(\"fnd\", \"cph\")) )", + "Description": "Interactive data tables for R, based on the 'React Table' JavaScript library. Provides an HTML widget that can be used in 'R Markdown' or 'Quarto' documents, 'Shiny' applications, or viewed from an R console.", + "License": "MIT + file LICENSE", + "URL": "https://glin.github.io/reactable/, https://github.com/glin/reactable", + "BugReports": "https://github.com/glin/reactable/issues", + "Depends": [ + "R (>= 3.1)" + ], + "Imports": [ + "digest", + "htmltools (>= 0.5.2)", + "htmlwidgets (>= 1.5.3)", + "jsonlite", + "reactR" + ], + "Suggests": [ + "covr", + "crosstalk", + "dplyr", + "fontawesome", + "knitr", + "leaflet", + "MASS", + "rmarkdown", + "shiny", + "sparkline", + "testthat", + "tippy", + "V8" + ], + "Encoding": "UTF-8", + "RoxygenNote": "7.2.1", + "Config/testthat/edition": "3", + "NeedsCompilation": "no", + "Author": "Greg Lin [aut, cre], Tanner Linsley [ctb, cph] (React Table library), Emotion team and other contributors [ctb, cph] (Emotion library), Kent Russell [ctb, cph] (reactR package), Ramnath Vaidyanathan [ctb, cph] (htmlwidgets package), Joe Cheng [ctb, cph] (htmlwidgets package), JJ Allaire [ctb, cph] (htmlwidgets package), Yihui Xie [ctb, cph] (htmlwidgets package), Kenton Russell [ctb, cph] (htmlwidgets package), Facebook, Inc. and its affiliates [ctb, cph] (React library), FormatJS [ctb, cph] (FormatJS libraries), Feross Aboukhadijeh, and other contributors [ctb, cph] (buffer library), Roman Shtylman [ctb, cph] (process library), James Halliday [ctb, cph] (stream-browserify library), Posit Software, PBC [fnd, cph]", + "Maintainer": "Greg Lin ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "readr": { + "Package": "readr", + "Version": "2.1.6", + "Source": "Repository", + "Title": "Read Rectangular Text Data", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Jim\", \"Hester\", role = \"aut\"), person(\"Romain\", \"Francois\", role = \"ctb\"), person(\"Jennifer\", \"Bryan\", , \"jenny@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-6983-2759\")), person(\"Shelby\", \"Bearrows\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"https://github.com/mandreyel/\", role = \"cph\", comment = \"mio library\"), person(\"Jukka\", \"Jylänki\", role = c(\"ctb\", \"cph\"), comment = \"grisu3 implementation\"), person(\"Mikkel\", \"Jørgensen\", role = c(\"ctb\", \"cph\"), comment = \"grisu3 implementation\") )", + "Description": "The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.", + "License": "MIT + file LICENSE", + "URL": "https://readr.tidyverse.org, https://github.com/tidyverse/readr", + "BugReports": "https://github.com/tidyverse/readr/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Imports": [ + "cli (>= 3.2.0)", + "clipr", + "crayon", + "hms (>= 0.4.1)", + "lifecycle (>= 0.2.0)", + "methods", + "R6", + "rlang", + "tibble", + "utils", + "vroom (>= 1.6.0)" + ], + "Suggests": [ + "covr", + "curl", + "datasets", + "knitr", + "rmarkdown", + "spelling", + "stringi", + "testthat (>= 3.2.0)", + "tzdb (>= 0.1.1)", + "waldo", + "withr", + "xml2" + ], + "LinkingTo": [ + "cpp11", + "tzdb (>= 0.1.1)" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse, tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/testthat/parallel": "false", + "Encoding": "UTF-8", + "Language": "en-US", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut], Jim Hester [aut], Romain Francois [ctb], Jennifer Bryan [aut, cre] (ORCID: ), Shelby Bearrows [ctb], Posit Software, PBC [cph, fnd], https://github.com/mandreyel/ [cph] (mio library), Jukka Jylänki [ctb, cph] (grisu3 implementation), Mikkel Jørgensen [ctb, cph] (grisu3 implementation)", + "Maintainer": "Jennifer Bryan ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "readxl": { + "Package": "readxl", + "Version": "1.4.5", + "Source": "Repository", + "Title": "Read Excel Files", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\", comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Jennifer\", \"Bryan\", , \"jenny@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-6983-2759\")), person(\"Posit, PBC\", role = c(\"cph\", \"fnd\"), comment = \"Copyright holder of all R code and all C/C++ code without explicit copyright attribution\"), person(\"Marcin\", \"Kalicinski\", role = c(\"ctb\", \"cph\"), comment = \"Author of included RapidXML code\"), person(\"Komarov Valery\", role = c(\"ctb\", \"cph\"), comment = \"Author of included libxls code\"), person(\"Christophe Leitienne\", role = c(\"ctb\", \"cph\"), comment = \"Author of included libxls code\"), person(\"Bob Colbert\", role = c(\"ctb\", \"cph\"), comment = \"Author of included libxls code\"), person(\"David Hoerl\", role = c(\"ctb\", \"cph\"), comment = \"Author of included libxls code\"), person(\"Evan Miller\", role = c(\"ctb\", \"cph\"), comment = \"Author of included libxls code\") )", + "Description": "Import excel files into R. Supports '.xls' via the embedded 'libxls' C library and '.xlsx' via the embedded 'RapidXML' C++ library . Works on Windows, Mac and Linux without external dependencies.", + "License": "MIT + file LICENSE", + "URL": "https://readxl.tidyverse.org, https://github.com/tidyverse/readxl", + "BugReports": "https://github.com/tidyverse/readxl/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Imports": [ + "cellranger", + "tibble (>= 2.0.1)", + "utils" + ], + "Suggests": [ + "covr", + "knitr", + "rmarkdown", + "testthat (>= 3.1.6)", + "withr" + ], + "LinkingTo": [ + "cpp11 (>= 0.4.0)", + "progress" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate, tidyverse", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "Note": "libxls v1.6.3 c199d13", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut] (), Jennifer Bryan [aut, cre] (), Posit, PBC [cph, fnd] (Copyright holder of all R code and all C/C++ code without explicit copyright attribution), Marcin Kalicinski [ctb, cph] (Author of included RapidXML code), Komarov Valery [ctb, cph] (Author of included libxls code), Christophe Leitienne [ctb, cph] (Author of included libxls code), Bob Colbert [ctb, cph] (Author of included libxls code), David Hoerl [ctb, cph] (Author of included libxls code), Evan Miller [ctb, cph] (Author of included libxls code)", + "Maintainer": "Jennifer Bryan ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "recipes": { + "Package": "recipes", + "Version": "1.3.1", + "Source": "Repository", + "Title": "Preprocessing and Feature Engineering Steps for Modeling", + "Authors@R": "c( person(\"Max\", \"Kuhn\", , \"max@posit.co\", role = c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Emil\", \"Hvitfeldt\", , \"emil.hvitfeldt@posit.co\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/tidymodels/recipes, https://recipes.tidymodels.org/", + "BugReports": "https://github.com/tidymodels/recipes/issues", + "Depends": [ + "dplyr (>= 1.1.0)", + "R (>= 4.1)" + ], + "Imports": [ + "cli", + "clock (>= 0.6.1)", + "generics (>= 0.1.2)", + "glue", + "gower", + "hardhat (>= 1.4.1)", + "ipred (>= 0.9-12)", + "lifecycle (>= 1.0.3)", + "lubridate (>= 1.8.0)", + "magrittr", + "Matrix", + "purrr (>= 1.0.0)", + "rlang (>= 1.1.0)", + "sparsevctrs (>= 0.3.3)", + "stats", + "tibble", + "tidyr (>= 1.0.0)", + "tidyselect (>= 1.2.0)", + "timeDate", + "utils", + "vctrs (>= 0.5.0)", + "withr" + ], + "Suggests": [ + "covr", + "ddalpha", + "dials (>= 1.2.0)", + "ggplot2", + "igraph", + "kernlab", + "knitr", + "methods", + "modeldata (>= 0.1.1)", + "parsnip (>= 1.2.0)", + "RANN", + "RcppRoll", + "rmarkdown", + "rpart", + "rsample", + "RSpectra", + "splines2", + "testthat (>= 3.0.0)", + "workflows", + "xml2" + ], + "VignetteBuilder": "knitr", + "RdMacros": "lifecycle", + "Config/Needs/website": "tidyverse/tidytemplate, rmarkdown", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-23", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Max Kuhn [aut, cre], Hadley Wickham [aut], Emil Hvitfeldt [aut], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Max Kuhn ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "reformulas": { + "Package": "reformulas", + "Version": "0.4.4", + "Source": "Repository", + "Title": "Machinery for Processing Random Effect Formulas", + "Authors@R": "c( person(given = \"Ben\", family = \"Bolker\", role = c(\"aut\", \"cre\"), email = \"bolker@mcmaster.ca\", comment=c(ORCID=\"0000-0002-2127-0443\")), person(\"Anna\", \"Ly\", role = \"ctb\", comment = c(ORCID = \"0000-0002-0210-0342\")) )", + "Description": "Takes formulas including random-effects components (formatted as in 'lme4', 'glmmTMB', etc.) and processes them. 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All function and argument names (and positions) are consistent, all functions deal with \"NA\"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.", + "License": "MIT + file LICENSE", + "URL": "https://stringr.tidyverse.org, https://github.com/tidyverse/stringr", + "BugReports": "https://github.com/tidyverse/stringr/issues", + "Depends": [ + "R (>= 4.1.0)" + ], + "Imports": [ + "cli", + "glue (>= 1.6.1)", + "lifecycle (>= 1.0.3)", + "magrittr", + "rlang (>= 1.0.0)", + "stringi (>= 1.5.3)", + "vctrs (>= 0.4.0)" + ], + "Suggests": [ + "covr", + "dplyr", + "gt", + "htmltools", + "htmlwidgets", + "knitr", + "rmarkdown", + "testthat (>= 3.0.0)", + "tibble" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/potools/style": "explicit", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "Hadley Wickham [aut, cre, cph], Posit Software, PBC [cph, fnd]", + "Maintainer": "Hadley Wickham ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "survival": { + "Package": "survival", + "Version": "3.8-3", + "Source": "Repository", + "Title": "Survival Analysis", + "Priority": "recommended", + "Date": "2024-12-17", + "Depends": [ + "R (>= 3.5.0)" + ], + "Imports": [ + "graphics", + "Matrix", + "methods", + "splines", + "stats", + "utils" + ], + "LazyData": "Yes", + "LazyDataCompression": "xz", + "ByteCompile": "Yes", + "Authors@R": "c(person(c(\"Terry\", \"M\"), \"Therneau\", email=\"therneau.terry@mayo.edu\", role=c(\"aut\", \"cre\")), person(\"Thomas\", \"Lumley\", role=c(\"ctb\", \"trl\"), comment=\"original S->R port and R maintainer until 2009\"), person(\"Atkinson\", \"Elizabeth\", role=\"ctb\"), person(\"Crowson\", \"Cynthia\", role=\"ctb\"))", + "Description": "Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.", + "License": "LGPL (>= 2)", + "URL": "https://github.com/therneau/survival", + "NeedsCompilation": "yes", + "Author": "Terry M Therneau [aut, cre], Thomas Lumley [ctb, trl] (original S->R port and R maintainer until 2009), Atkinson Elizabeth [ctb], Crowson Cynthia [ctb]", + "Maintainer": "Terry M Therneau ", + "Repository": "CRAN" + }, + "sys": { + "Package": "sys", + "Version": "3.4.3", + "Source": "Repository", + "Type": "Package", + "Title": "Powerful and Reliable Tools for Running System Commands in R", + "Authors@R": "c(person(\"Jeroen\", \"Ooms\", role = c(\"aut\", \"cre\"), email = \"jeroenooms@gmail.com\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = \"ctb\"))", + "Description": "Drop-in replacements for the base system2() function with fine control and consistent behavior across platforms. Supports clean interruption, timeout, background tasks, and streaming STDIN / STDOUT / STDERR over binary or text connections. Arguments on Windows automatically get encoded and quoted to work on different locales.", + "License": "MIT + file LICENSE", + "URL": "https://jeroen.r-universe.dev/sys", + "BugReports": "https://github.com/jeroen/sys/issues", + "Encoding": "UTF-8", + "RoxygenNote": "7.1.1", + "Suggests": [ + "unix (>= 1.4)", + "spelling", + "testthat" + ], + "Language": "en-US", + "NeedsCompilation": "yes", + "Author": "Jeroen Ooms [aut, cre] (), Gábor Csárdi [ctb]", + "Maintainer": "Jeroen Ooms ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "systemfonts": { + "Package": "systemfonts", + "Version": "1.3.1", + "Source": "Repository", + "Type": "Package", + "Title": "System Native Font Finding", + "Authors@R": "c( person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Jeroen\", \"Ooms\", , \"jeroen@berkeley.edu\", role = \"aut\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"Devon\", \"Govett\", role = \"aut\", comment = \"Author of font-manager\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "Provides system native access to the font catalogue. As font handling varies between systems it is difficult to correctly locate installed fonts across different operating systems. The 'systemfonts' package provides bindings to the native libraries on Windows, macOS and Linux for finding font files that can then be used further by e.g. graphic devices. 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This package introduces 'tailor' objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) . Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the 'tidymodels' framework.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/tidymodels/tailor, https://tailor.tidymodels.org", + "BugReports": "https://github.com/tidymodels/tailor/issues", + "Depends": [ + "R (>= 4.1)" + ], + "Imports": [ + "cli", + "dplyr", + "generics", + "hardhat", + "purrr", + "rlang (>= 1.1.0)", + "tibble", + "tidyselect", + "vctrs" + ], + "Suggests": [ + "betacal", + "dials (>= 1.4.1)", + "mgcv", + "modeldata", + "probably (>= 1.1.0)", + "testthat (>= 3.0.0)" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-29", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Simon Couch [aut], Hannah Frick [aut], Emil HvitFeldt [aut], Max Kuhn [aut, cre] (ORCID: ), Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Max Kuhn ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "tarchetypes": { + "Package": "tarchetypes", + "Version": "0.14.0", + "Source": "Repository", + "Title": "Archetypes for Targets", + "Description": "Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) .", + "License": "MIT + file LICENSE", + "URL": "https://docs.ropensci.org/tarchetypes/, https://github.com/ropensci/tarchetypes", + "BugReports": "https://github.com/ropensci/tarchetypes/issues", + "Authors@R": "c( person( given = c(\"William\", \"Michael\"), family = \"Landau\", role = c(\"aut\", \"cre\"), email = \"will.landau.oss@gmail.com\", comment = c(ORCID = \"0000-0003-1878-3253\") ), person( given = \"Rudolf\", family = \"Siegel\", role = \"ctb\", comment = c(ORCID = \"0000-0002-6021-804X\") ), person( given = \"Samantha\", family = \"Oliver\", role = \"rev\", comment = c(ORCID = \"0000-0001-5668-1165\") ), person( given = \"Tristan\", family = \"Mahr\", role = \"rev\", comment = c(ORCID = \"0000-0002-8890-5116\") ), person( family = \"Eli Lilly and Company\", role = c(\"cph\", \"fnd\") ))", + "Depends": [ + "R (>= 4.1.0)" + ], + "Imports": [ + "dplyr (>= 1.0.0)", + "fs (>= 1.4.2)", + "parallel", + "rlang (>= 0.4.7)", + "secretbase (>= 0.4.0)", + "targets (>= 1.6.0)", + "tibble (>= 3.0.1)", + "tidyselect (>= 1.1.0)", + "utils", + "vctrs (>= 0.3.4)", + "withr (>= 2.1.2)" + ], + "Suggests": [ + "curl (>= 4.3)", + "knitr (>= 1.28)", + "nanoparquet", + "parsermd", + "quarto (>= 1.4)", + "rmarkdown (>= 2.1)", + "testthat (>= 3.0.0)", + "xml2 (>= 1.3.2)" + ], + "Encoding": "UTF-8", + "Language": "en-US", + "Config/testthat/edition": "3", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "William Michael Landau [aut, cre] (ORCID: ), Rudolf Siegel [ctb] (ORCID: ), Samantha Oliver [rev] (ORCID: ), Tristan Mahr [rev] (ORCID: ), Eli Lilly and Company [cph, fnd]", + "Maintainer": "William Michael Landau ", + "Repository": "P3M" + }, + "targets": { + "Package": "targets", + "Version": "1.12.0", + "Source": "Repository", + "Title": "Dynamic Function-Oriented 'Make'-Like Declarative Pipelines", + "Description": "Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, ).", + "License": "MIT + file LICENSE", + "URL": "https://docs.ropensci.org/targets/, https://github.com/ropensci/targets", + "BugReports": "https://github.com/ropensci/targets/issues", + "Authors@R": "c( person( given = c(\"William\", \"Michael\"), family = \"Landau\", role = c(\"aut\", \"cre\"), email = \"will.landau.oss@gmail.com\", comment = c(ORCID = \"0000-0003-1878-3253\") ), person( given = c(\"Matthew\", \"T.\"), family = \"Warkentin\", role = \"ctb\" ), person( given = \"Mark\", family = \"Edmondson\", email = \"r@sunholo.com\", role = \"ctb\", comment = c(ORCID = \"0000-0002-8434-3881\") ), person( given = \"Samantha\", family = \"Oliver\", role = \"rev\", comment = c(ORCID = \"0000-0001-5668-1165\") ), person( given = \"Tristan\", family = \"Mahr\", role = \"rev\", comment = c(ORCID = \"0000-0002-8890-5116\") ), person( family = \"Eli Lilly and Company\", role = c(\"cph\", \"fnd\") ))", + "Depends": [ + "R (>= 3.5.0)" + ], + "Imports": [ + "base64url (>= 1.4)", + "callr (>= 3.7.0)", + "cli (>= 2.0.2)", + "codetools (>= 0.2.16)", + "data.table (>= 1.16.0)", + "igraph (>= 2.0.0)", + "knitr (>= 1.34)", + "prettyunits (>= 1.1.0)", + "ps (>= 1.8.0)", + "R6 (>= 2.4.1)", + "rlang (>= 1.0.0)", + "secretbase (>= 0.5.0)", + "stats", + "tibble (>= 3.0.1)", + "tidyselect (>= 1.1.0)", + "tools", + "utils", + "vctrs (>= 0.2.4)", + "yaml (>= 2.2.1)" + ], + "Suggests": [ + "autometric (>= 0.1.0)", + "bslib", + "clustermq (>= 0.9.2)", + "crew (>= 0.9.0)", + "curl (>= 4.3)", + "DT (>= 0.14)", + "dplyr (>= 1.0.0)", + "fst (>= 0.9.2)", + "future (>= 1.19.1)", + "future.batchtools (>= 0.9.0)", + "future.callr (>= 0.6.0)", + "gargle (>= 1.2.0)", + "googleCloudStorageR (>= 0.7.0)", + "gt (>= 0.2.2)", + "keras (>= 2.2.5.0)", + "markdown (>= 1.1)", + "nanonext (>= 0.12.0)", + "rmarkdown (>= 2.4)", + "parallelly (>= 1.35.0)", + "paws.common (>= 0.6.4)", + "paws.storage (>= 0.4.0)", + "pkgload (>= 1.1.0)", + "processx (>= 3.4.3)", + "qs2", + "reprex (>= 2.0.0)", + "rstudioapi (>= 0.11)", + "R.utils (>= 2.6.0)", + "shiny (>= 1.5.0)", + "shinybusy (>= 0.2.2)", + "shinyWidgets (>= 0.5.4)", + "tarchetypes", + "testthat (>= 3.0.0)", + "torch (>= 0.1.0)", + "usethis (>= 1.6.3)", + "visNetwork (>= 2.1.2)" + ], + "Encoding": "UTF-8", + "Language": "en-US", + "VignetteBuilder": "knitr", + "Config/testthat/edition": "3", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "William Michael Landau [aut, cre] (ORCID: ), Matthew T. Warkentin [ctb], Mark Edmondson [ctb] (ORCID: ), Samantha Oliver [rev] (ORCID: ), Tristan Mahr [rev] (ORCID: ), Eli Lilly and Company [cph, fnd]", + "Maintainer": "William Michael Landau ", + "Repository": "P3M" + }, + "tensorA": { + "Package": "tensorA", + "Version": "0.36.2.1", + "Source": "Repository", + "Date": "2020-11-13", + "Title": "Advanced Tensor Arithmetic with Named Indices", + "Author": "K. Gerald van den Boogaart ", + "Maintainer": "K. Gerald van den Boogaart ", + "Depends": [ + "R (>= 2.2.0)", + "stats" + ], + "Description": "Provides convenience functions for advanced linear algebra with tensors and computation with data sets of tensors on a higher level abstraction. It includes Einstein and Riemann summing conventions, dragging, co- and contravariate indices, parallel computations on sequences of tensors.", + "License": "GPL (>= 2)", + "URL": "http://www.stat.boogaart.de/tensorA/", + "NeedsCompilation": "yes", + "Repository": "P3M", + "Encoding": "UTF-8" + }, + "testthat": { + "Package": "testthat", + "Version": "3.3.2", + "Source": "Repository", + "Title": "Unit Testing for R", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"R Core team\", role = \"ctb\", comment = \"Implementation of utils::recover()\") )", + "Description": "Software testing is important, but, in part because it is frustrating and boring, many of us avoid it. 'testthat' is a testing framework for R that is easy to learn and use, and integrates with your existing 'workflow'.", + "License": "MIT + file LICENSE", + "URL": "https://testthat.r-lib.org, https://github.com/r-lib/testthat", + "BugReports": "https://github.com/r-lib/testthat/issues", + "Depends": [ + "R (>= 4.1.0)" + ], + "Imports": [ + "brio (>= 1.1.5)", + "callr (>= 3.7.6)", + "cli (>= 3.6.5)", + "desc (>= 1.4.3)", + "evaluate (>= 1.0.4)", + "jsonlite (>= 2.0.0)", + "lifecycle (>= 1.0.4)", + "magrittr (>= 2.0.3)", + "methods", + "pkgload (>= 1.4.0)", + "praise (>= 1.0.0)", + "processx (>= 3.8.6)", + "ps (>= 1.9.1)", + "R6 (>= 2.6.1)", + "rlang (>= 1.1.6)", + "utils", + "waldo (>= 0.6.2)", + "withr (>= 3.0.2)" + ], + "Suggests": [ + "covr", + "curl (>= 0.9.5)", + "diffviewer (>= 0.1.0)", + "digest (>= 0.6.33)", + "gh", + "knitr", + "otel", + "otelsdk", + "rmarkdown", + "rstudioapi", + "S7", + "shiny", + "usethis", + "vctrs (>= 0.1.0)", + "xml2" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/testthat/parallel": "true", + "Config/testthat/start-first": "watcher, parallel*", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd], R Core team [ctb] (Implementation of utils::recover())", + "Maintainer": "Hadley Wickham ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "textshaping": { + "Package": "textshaping", + "Version": "1.0.4", + "Source": "Repository", + "Title": "Bindings to the 'HarfBuzz' and 'Fribidi' Libraries for Text Shaping", + "Authors@R": "c( person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "Provides access to the text shaping functionality in the 'HarfBuzz' library and the bidirectional algorithm in the 'Fribidi' library. 'textshaping' is a low-level utility package mainly for graphic devices that expands upon the font tool-set provided by the 'systemfonts' package.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/textshaping", + "BugReports": "https://github.com/r-lib/textshaping/issues", + "Depends": [ + "R (>= 3.2.0)" + ], + "Imports": [ + "lifecycle", + "stats", + "stringi", + "systemfonts (>= 1.3.0)", + "utils" + ], + "Suggests": [ + "covr", + "grDevices", + "grid", + "knitr", + "rmarkdown", + "testthat (>= 3.0.0)" + ], + "LinkingTo": [ + "cpp11 (>= 0.2.1)", + "systemfonts (>= 1.0.0)" + ], + "VignetteBuilder": "knitr", + "Config/build/compilation-database": "true", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-23", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "SystemRequirements": "freetype2, harfbuzz, fribidi", + "NeedsCompilation": "yes", + "Author": "Thomas Lin Pedersen [cre, aut] (ORCID: ), Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Thomas Lin Pedersen ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "themis": { + "Package": "themis", + "Version": "1.0.3", + "Source": "Repository", + "Title": "Extra Recipes Steps for Dealing with Unbalanced Data", + "Authors@R": "c( person(\"Emil\", \"Hvitfeldt\", , \"emil.hvitfeldt@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-0679-1945\")), person(given = \"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 , BorderlineSMOTE 2005 and ADASYN 2008 . Or by decreasing the number of majority cases using NearMiss 2003 or Tomek link removal 1976 .", + "License": "MIT + file LICENSE", + "URL": "https://github.com/tidymodels/themis, https://themis.tidymodels.org", + "BugReports": "https://github.com/tidymodels/themis/issues", + "Depends": [ + "R (>= 3.6)", + "recipes (>= 1.1.0)" + ], + "Imports": [ + "cli", + "gower", + "lifecycle (>= 1.0.3)", + "dplyr", + "generics (>= 0.1.0)", + "purrr", + "RANN", + "rlang (>= 1.1.0)", + "ROSE", + "tibble", + "withr", + "glue", + "hardhat", + "vctrs" + ], + "Suggests": [ + "covr", + "dials (>= 1.2.0)", + "ggplot2", + "modeldata", + "testthat (>= 3.0.0)" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Emil Hvitfeldt [aut, cre] (), Posit Software, PBC [cph, fnd]", + "Maintainer": "Emil Hvitfeldt ", + "Repository": "P3M" + }, + "threejs": { + "Package": "threejs", + "Version": "0.3.4", + "Source": "Repository", + "Type": "Package", + "Title": "Interactive 3D Scatter Plots, Networks and Globes", + "Description": "Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library ().", + "Date": "2025-04-19", + "Authors@R": "c( person(\"B. 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These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) .", + "License": "MIT + file LICENSE", + "URL": "https://tidyposterior.tidymodels.org, https://github.com/tidymodels/tidyposterior", + "BugReports": "https://github.com/tidymodels/tidyposterior/issues", + "Depends": [ + "R (>= 4.1)" + ], + "Imports": [ + "dplyr (> 1.0.0)", + "generics", + "ggplot2 (>= 3.5.2)", + "purrr", + "rlang", + "rsample (>= 0.0.2)", + "rstanarm (>= 2.21.1)", + "stats", + "tibble", + "tidyr (>= 0.7.1)", + "tune (>= 0.2.0)", + "utils", + "vctrs (>= 0.3.0)", + "workflowsets" + ], + "Suggests": [ + "covr", + "knitr", + "parsnip", + "rmarkdown", + "splines2", + "testthat (>= 3.0.0)", + "yardstick" + ], + "VignetteBuilder": "knitr", + "ByteCompile": "true", + "Config/Needs/website": "tidymodels, tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-27", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Max Kuhn [aut, cre] (ORCID: ), Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Max Kuhn ", + "Repository": "P3M" + }, + "tidyr": { + "Package": "tidyr", + "Version": "1.3.2", + "Source": "Repository", + "Title": "Tidy Messy Data", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\")), person(\"Davis\", \"Vaughan\", , \"davis@posit.co\", role = \"aut\"), person(\"Maximilian\", \"Girlich\", role = \"aut\"), person(\"Kevin\", \"Ushey\", , \"kevin@posit.co\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 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Beyond these standards it provides the \"Financial Center\" concept which allows to handle data records collected in different time zones and mix them up to have always the proper time stamps with respect to your personal financial center, or alternatively to the GMT reference time. 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Ryan [aut, cph], Joshua M. Ulrich [cre, aut], Ross Bennett [ctb], Corwin Joy [ctb]", + "Maintainer": "Joshua M. Ulrich ", + "Repository": "P3M" + }, + "yaml": { + "Package": "yaml", + "Version": "2.3.12", + "Source": "Repository", + "Type": "Package", + "Title": "Methods to Convert R Data to YAML and Back", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"cre\", comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Shawn\", \"Garbett\", , \"shawn.garbett@vumc.org\", role = \"ctb\", comment = c(ORCID = \"0000-0003-4079-5621\")), person(\"Jeremy\", \"Stephens\", role = c(\"aut\", \"ctb\")), person(\"Kirill\", \"Simonov\", role = \"aut\"), person(\"Yihui\", \"Xie\", role = \"ctb\", comment = c(ORCID = \"0000-0003-0645-5666\")), person(\"Zhuoer\", \"Dong\", role = \"ctb\"), person(\"Jeffrey\", \"Horner\", role = \"ctb\"), person(\"reikoch\", role = \"ctb\"), person(\"Will\", \"Beasley\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5613-5006\")), person(\"Brendan\", \"O'Connor\", role = \"ctb\"), person(\"Michael\", \"Quinn\", role = \"ctb\"), person(\"Charlie\", \"Gao\", role = \"ctb\"), person(c(\"Gregory\", \"R.\"), \"Warnes\", role = \"ctb\"), person(c(\"Zhian\", \"N.\"), \"Kamvar\", role = \"ctb\") )", + "Description": "Implements the 'libyaml' 'YAML' 1.1 parser and emitter () for R.", + "License": "BSD_3_clause + file LICENSE", + "URL": "https://yaml.r-lib.org, https://github.com/r-lib/yaml/", + "BugReports": "https://github.com/r-lib/yaml/issues", + "Suggests": [ + "knitr", + "rmarkdown", + "testthat (>= 3.0.0)" + ], + "Config/testthat/edition": "3", + "Config/Needs/website": "tidyverse/tidytemplate", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "VignetteBuilder": "knitr", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [cre] (ORCID: ), Shawn Garbett [ctb] (ORCID: ), Jeremy Stephens [aut, ctb], Kirill Simonov [aut], Yihui Xie [ctb] (ORCID: ), Zhuoer Dong [ctb], Jeffrey Horner [ctb], reikoch [ctb], Will Beasley [ctb] (ORCID: ), Brendan O'Connor [ctb], Michael Quinn [ctb], Charlie Gao [ctb], Gregory R. Warnes [ctb], Zhian N. Kamvar [ctb]", + "Maintainer": "Hadley Wickham ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "yardstick": { + "Package": "yardstick", + "Version": "1.3.2", + "Source": "Repository", + "Type": "Package", + "Title": "Tidy Characterizations of Model Performance", + "Authors@R": "c( person(\"Max\", \"Kuhn\", , \"max@posit.co\", role = \"aut\"), person(\"Davis\", \"Vaughan\", , \"davis@posit.co\", role = \"aut\"), person(\"Emil\", \"Hvitfeldt\", , \"emil.hvitfeldt@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-0679-1945\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).", + "License": "MIT + file LICENSE", + "URL": "https://github.com/tidymodels/yardstick, https://yardstick.tidymodels.org", + "BugReports": "https://github.com/tidymodels/yardstick/issues", + "Depends": [ + "R (>= 3.6.0)" + ], + "Imports": [ + "cli", + "dplyr (>= 1.1.0)", + "generics (>= 0.1.2)", + "hardhat (>= 1.3.0)", + "lifecycle (>= 1.0.3)", + "rlang (>= 1.1.4)", + "tibble", + "tidyselect (>= 1.2.0)", + "utils", + "vctrs (>= 0.5.0)", + "withr" + ], + "Suggests": [ + "covr", + "crayon", + "ggplot2", + "knitr", + "probably (>= 1.0.0)", + "rmarkdown", + "survival (>= 3.5-0)", + "testthat (>= 3.0.0)", + "tidyr" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2024-10-24", + "Encoding": "UTF-8", + "LazyData": "true", + "RoxygenNote": "7.3.2", + "Collate": "'aaa-metrics.R' 'import-standalone-types-check.R' 'aaa-new.R' 'aaa.R' 'check-metric.R' 'class-accuracy.R' 'class-bal_accuracy.R' 'class-detection_prevalence.R' 'class-f_meas.R' 'class-j_index.R' 'class-kap.R' 'class-mcc.R' 'class-npv.R' 'class-ppv.R' 'class-precision.R' 'class-recall.R' 'class-sens.R' 'class-spec.R' 'conf_mat.R' 'data.R' 'deprecated-prob_helpers.R' 'deprecated-template.R' 'estimator-helpers.R' 'event-level.R' 'fair-aaa.R' 'fair-demographic_parity.R' 'fair-equal_opportunity.R' 'fair-equalized_odds.R' 'import-standalone-obj-type.R' 'import-standalone-survival.R' 'metric-tweak.R' 'misc.R' 'missings.R' 'num-ccc.R' 'num-huber_loss.R' 'num-iic.R' 'num-mae.R' 'num-mape.R' 'num-mase.R' 'num-mpe.R' 'num-msd.R' 'num-poisson_log_loss.R' 'num-pseudo_huber_loss.R' 'num-rmse.R' 'num-rpd.R' 'num-rpiq.R' 'num-rsq.R' 'num-rsq_trad.R' 'num-smape.R' 'prob-average_precision.R' 'prob-binary-thresholds.R' 'prob-brier_class.R' 'prob-classification_cost.R' 'prob-gain_capture.R' 'prob-gain_curve.R' 'prob-helpers.R' 'prob-lift_curve.R' 'prob-mn_log_loss.R' 'prob-pr_auc.R' 'prob-pr_curve.R' 'prob-roc_auc.R' 'prob-roc_aunp.R' 'prob-roc_aunu.R' 'prob-roc_curve.R' 'reexports.R' 'surv-brier_survival.R' 'surv-brier_survival_integrated.R' 'surv-concordance_survival.R' 'surv-roc_auc_survival.R' 'surv-roc_curve_survival.R' 'template.R' 'validation.R' 'yardstick-package.R'", + "NeedsCompilation": "yes", + "Author": "Max Kuhn [aut], Davis Vaughan [aut], Emil Hvitfeldt [aut, cre] (), Posit Software, PBC [cph, fnd]", + "Maintainer": "Emil Hvitfeldt ", + "Repository": "https://p3m.dev/cran/__linux__/noble/latest" + }, + "zip": { + "Package": "zip", + "Version": "2.3.3", + "Source": "Repository", + "Title": "Cross-Platform 'zip' Compression", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Kuba\", \"Podgórski\", role = \"ctb\"), person(\"Rich\", \"Geldreich\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "Cross-Platform 'zip' Compression Library. A replacement for the 'zip' function, that does not require any additional external tools on any platform.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/zip, https://r-lib.github.io/zip/", + "BugReports": "https://github.com/r-lib/zip/issues", + "Suggests": [ + "covr", + "pillar", + "processx", + "R6", + "testthat", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-05-07", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2.9000", + "NeedsCompilation": "yes", + "Author": "Gábor Csárdi [aut, cre], Kuba Podgórski [ctb], Rich Geldreich [ctb], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Gábor Csárdi ", + "Repository": "https://packagemanager.posit.co/cran/latest" + }, + "zoo": { + "Package": "zoo", + "Version": "1.8-15", + "Source": "Repository", + "Date": "2025-12-15", + "Title": "S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)", + "Authors@R": "c(person(given = \"Achim\", family = \"Zeileis\", role = c(\"aut\", \"cre\"), email = \"Achim.Zeileis@R-project.org\", comment = c(ORCID = \"0000-0003-0918-3766\")), person(given = \"Gabor\", family = \"Grothendieck\", role = \"aut\", email = \"ggrothendieck@gmail.com\"), person(given = c(\"Jeffrey\", \"A.\"), family = \"Ryan\", role = \"aut\", email = \"jeff.a.ryan@gmail.com\"), person(given = c(\"Joshua\", \"M.\"), family = \"Ulrich\", role = \"ctb\", email = \"josh.m.ulrich@gmail.com\"), person(given = \"Felix\", family = \"Andrews\", role = \"ctb\", email = \"felix@nfrac.org\"))", + "Description": "An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.", + "Depends": [ + "R (>= 3.1.0)", + "stats" + ], + "Suggests": [ + "AER", + "coda", + "chron", + "ggplot2 (>= 3.5.0)", + "mondate", + "scales", + "stinepack", + "strucchange", + "timeDate", + "timeSeries", + "tinyplot", + "tis", + "tseries", + "xts" + ], + "Imports": [ + "utils", + "graphics", + "grDevices", + "lattice (>= 0.20-27)" + ], + "License": "GPL-2 | GPL-3", + "URL": "https://zoo.R-Forge.R-project.org/", + "NeedsCompilation": "yes", + "Author": "Achim Zeileis [aut, cre] (ORCID: ), Gabor Grothendieck [aut], Jeffrey A. Ryan [aut], Joshua M. Ulrich [ctb], Felix Andrews [ctb]", + "Maintainer": "Achim Zeileis ", + "Repository": "P3M", + "Encoding": "UTF-8" + } + } +} diff --git a/renv/.gitignore b/renv/.gitignore new file mode 100644 index 0000000..0ec0cbb --- /dev/null +++ b/renv/.gitignore @@ -0,0 +1,7 @@ +library/ +local/ +cellar/ +lock/ +python/ +sandbox/ +staging/ diff --git a/renv/activate.R b/renv/activate.R new file mode 100644 index 0000000..2753ae5 --- /dev/null +++ b/renv/activate.R @@ -0,0 +1,1334 @@ + +local({ + + # the requested version of renv + version <- "1.1.5" + attr(version, "sha") <- NULL + + # the project directory + project <- Sys.getenv("RENV_PROJECT") + if (!nzchar(project)) + project <- getwd() + + # use start-up diagnostics if enabled + diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALSE") + if (diagnostics) { + start <- Sys.time() + profile <- tempfile("renv-startup-", fileext = ".Rprof") + utils::Rprof(profile) + on.exit({ + utils::Rprof(NULL) + elapsed <- signif(difftime(Sys.time(), start, units = "auto"), digits = 2L) + writeLines(sprintf("- renv took %s to run the autoloader.", format(elapsed))) + writeLines(sprintf("- Profile: %s", profile)) + print(utils::summaryRprof(profile)) + }, add = TRUE) + } + + # figure out whether the autoloader is enabled + enabled <- local({ + + # first, check config option + override <- getOption("renv.config.autoloader.enabled") + if (!is.null(override)) + return(override) + + # if we're being run in a context where R_LIBS is already set, + # don't load -- presumably we're being run as a sub-process and + # the parent process has already set up library paths for us + rcmd <- Sys.getenv("R_CMD", unset = NA) + rlibs <- Sys.getenv("R_LIBS", unset = NA) + if (!is.na(rlibs) && !is.na(rcmd)) + return(FALSE) + + # next, check environment variables + # prefer using the configuration one in the future + envvars <- c( + "RENV_CONFIG_AUTOLOADER_ENABLED", + "RENV_AUTOLOADER_ENABLED", + "RENV_ACTIVATE_PROJECT" + ) + + for (envvar in envvars) { + envval <- Sys.getenv(envvar, unset = NA) + if (!is.na(envval)) + return(tolower(envval) %in% c("true", "t", "1")) + } + + # enable by default + TRUE + + }) + + # bail if we're not enabled + if (!enabled) { + + # if we're not enabled, we might still need to manually load + # the user profile here + profile <- Sys.getenv("R_PROFILE_USER", unset = "~/.Rprofile") + if (file.exists(profile)) { + cfg <- Sys.getenv("RENV_CONFIG_USER_PROFILE", unset = "TRUE") + if (tolower(cfg) %in% c("true", "t", "1")) + sys.source(profile, envir = globalenv()) + } + + return(FALSE) + + } + + # avoid recursion + if (identical(getOption("renv.autoloader.running"), TRUE)) { + warning("ignoring recursive attempt to run renv autoloader") + return(invisible(TRUE)) + } + + # signal that we're loading renv during R startup + options(renv.autoloader.running = TRUE) + on.exit(options(renv.autoloader.running = NULL), add = TRUE) + + # signal that we've consented to use renv + options(renv.consent = TRUE) + + # load the 'utils' package eagerly -- this ensures that renv shims, which + # mask 'utils' packages, will come first on the search path + library(utils, lib.loc = .Library) + + # unload renv if it's already been loaded + if ("renv" %in% loadedNamespaces()) + unloadNamespace("renv") + + # load bootstrap tools + ansify <- function(text) { + if (renv_ansify_enabled()) + renv_ansify_enhanced(text) + else + renv_ansify_default(text) + } + + renv_ansify_enabled <- function() { + + override <- Sys.getenv("RENV_ANSIFY_ENABLED", unset = NA) + if (!is.na(override)) + return(as.logical(override)) + + pane <- Sys.getenv("RSTUDIO_CHILD_PROCESS_PANE", unset = NA) + if (identical(pane, "build")) + return(FALSE) + + testthat <- Sys.getenv("TESTTHAT", unset = "false") + if (tolower(testthat) %in% "true") + return(FALSE) + + iderun <- Sys.getenv("R_CLI_HAS_HYPERLINK_IDE_RUN", unset = "false") + if (tolower(iderun) %in% "false") + return(FALSE) + + TRUE + + } + + renv_ansify_default <- function(text) { + text + } + + renv_ansify_enhanced <- function(text) { + + # R help links + pattern <- "`\\?(renv::(?:[^`])+)`" + replacement <- "`\033]8;;x-r-help:\\1\a?\\1\033]8;;\a`" + text <- gsub(pattern, replacement, text, perl = TRUE) + + # runnable code + pattern <- "`(renv::(?:[^`])+)`" + replacement <- "`\033]8;;x-r-run:\\1\a\\1\033]8;;\a`" + text <- gsub(pattern, replacement, text, perl = TRUE) + + # return ansified text + text + + } + + renv_ansify_init <- function() { + + envir <- renv_envir_self() + if (renv_ansify_enabled()) + assign("ansify", renv_ansify_enhanced, envir = envir) + else + assign("ansify", renv_ansify_default, envir = envir) + + } + + `%||%` <- function(x, y) { + if (is.null(x)) y else x + } + + catf <- function(fmt, ..., appendLF = TRUE) { + + quiet <- getOption("renv.bootstrap.quiet", default = FALSE) + if (quiet) + return(invisible()) + + msg <- sprintf(fmt, ...) + cat(msg, file = stdout(), sep = if (appendLF) "\n" else "") + + invisible(msg) + + } + + header <- function(label, + ..., + prefix = "#", + suffix = "-", + n = min(getOption("width"), 78)) + { + label <- sprintf(label, ...) + n <- max(n - nchar(label) - nchar(prefix) - 2L, 8L) + if (n <= 0) + return(paste(prefix, label)) + + tail <- paste(rep.int(suffix, n), collapse = "") + paste0(prefix, " ", label, " ", tail) + + } + + heredoc <- function(text, leave = 0) { + + # remove leading, trailing whitespace + trimmed <- gsub("^\\s*\\n|\\n\\s*$", "", text) + + # split into lines + lines <- strsplit(trimmed, "\n", fixed = TRUE)[[1L]] + + # compute common indent + indent <- regexpr("[^[:space:]]", lines) + common <- min(setdiff(indent, -1L)) - leave + text <- paste(substring(lines, common), collapse = "\n") + + # substitute in ANSI links for executable renv code + ansify(text) + + } + + bootstrap <- function(version, library) { + + friendly <- renv_bootstrap_version_friendly(version) + section <- header(sprintf("Bootstrapping renv %s", friendly)) + catf(section) + + # attempt to download renv + catf("- Downloading renv ... ", appendLF = FALSE) + withCallingHandlers( + tarball <- renv_bootstrap_download(version), + error = function(err) { + catf("FAILED") + stop("failed to download:\n", conditionMessage(err)) + } + ) + catf("OK") + on.exit(unlink(tarball), add = TRUE) + + # now attempt to install + catf("- Installing renv ... ", appendLF = FALSE) + withCallingHandlers( + status <- renv_bootstrap_install(version, tarball, library), + error = function(err) { + catf("FAILED") + stop("failed to install:\n", conditionMessage(err)) + } + ) + catf("OK") + + # add empty line to break up bootstrapping from normal output + catf("") + + return(invisible()) + } + + renv_bootstrap_tests_running <- function() { + getOption("renv.tests.running", default = FALSE) + } + + renv_bootstrap_repos <- function() { + + # get CRAN repository + cran <- getOption("renv.repos.cran", "https://cloud.r-project.org") + + # check for repos override + repos <- Sys.getenv("RENV_CONFIG_REPOS_OVERRIDE", unset = NA) + if (!is.na(repos)) { + + # check for RSPM; if set, use a fallback repository for renv + rspm <- Sys.getenv("RSPM", unset = NA) + if (identical(rspm, repos)) + repos <- c(RSPM = rspm, CRAN = cran) + + return(repos) + + } + + # check for lockfile repositories + repos <- tryCatch(renv_bootstrap_repos_lockfile(), error = identity) + if (!inherits(repos, "error") && length(repos)) + return(repos) + + # retrieve current repos + repos <- getOption("repos") + + # ensure @CRAN@ entries are resolved + repos[repos == "@CRAN@"] <- cran + + # add in renv.bootstrap.repos if set + default <- c(FALLBACK = "https://cloud.r-project.org") + extra <- getOption("renv.bootstrap.repos", default = default) + repos <- c(repos, extra) + + # remove duplicates that might've snuck in + dupes <- duplicated(repos) | duplicated(names(repos)) + repos[!dupes] + + } + + renv_bootstrap_repos_lockfile <- function() { + + lockpath <- Sys.getenv("RENV_PATHS_LOCKFILE", unset = "renv.lock") + if (!file.exists(lockpath)) + return(NULL) + + lockfile <- tryCatch(renv_json_read(lockpath), error = identity) + if (inherits(lockfile, "error")) { + warning(lockfile) + return(NULL) + } + + repos <- lockfile$R$Repositories + if (length(repos) == 0) + return(NULL) + + keys <- vapply(repos, `[[`, "Name", FUN.VALUE = character(1)) + vals <- vapply(repos, `[[`, "URL", FUN.VALUE = character(1)) + names(vals) <- keys + + return(vals) + + } + + renv_bootstrap_download <- function(version) { + + sha <- attr(version, "sha", exact = TRUE) + + methods <- if (!is.null(sha)) { + + # attempting to bootstrap a development version of renv + c( + function() renv_bootstrap_download_tarball(sha), + function() renv_bootstrap_download_github(sha) + ) + + } else { + + # attempting to bootstrap a release version of renv + c( + function() renv_bootstrap_download_tarball(version), + function() renv_bootstrap_download_cran_latest(version), + function() renv_bootstrap_download_cran_archive(version) + ) + + } + + for (method in methods) { + path <- tryCatch(method(), error = identity) + if (is.character(path) && file.exists(path)) + return(path) + } + + stop("All download methods failed") + + } + + renv_bootstrap_download_impl <- function(url, destfile) { + + mode <- "wb" + + # https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17715 + fixup <- + Sys.info()[["sysname"]] == "Windows" && + substring(url, 1L, 5L) == "file:" + + if (fixup) + mode <- "w+b" + + args <- list( + url = url, + destfile = destfile, + mode = mode, + quiet = TRUE + ) + + if ("headers" %in% names(formals(utils::download.file))) { + headers <- renv_bootstrap_download_custom_headers(url) + if (length(headers) && is.character(headers)) + args$headers <- headers + } + + do.call(utils::download.file, args) + + } + + renv_bootstrap_download_custom_headers <- function(url) { + + headers <- getOption("renv.download.headers") + if (is.null(headers)) + return(character()) + + if (!is.function(headers)) + stopf("'renv.download.headers' is not a function") + + headers <- headers(url) + if (length(headers) == 0L) + return(character()) + + if (is.list(headers)) + headers <- unlist(headers, recursive = FALSE, use.names = TRUE) + + ok <- + is.character(headers) && + is.character(names(headers)) && + all(nzchar(names(headers))) + + if (!ok) + stop("invocation of 'renv.download.headers' did not return a named character vector") + + headers + + } + + renv_bootstrap_download_cran_latest <- function(version) { + + spec <- renv_bootstrap_download_cran_latest_find(version) + type <- spec$type + repos <- spec$repos + + baseurl <- utils::contrib.url(repos = repos, type = type) + ext <- if (identical(type, "source")) + ".tar.gz" + else if (Sys.info()[["sysname"]] == "Windows") + ".zip" + else + ".tgz" + name <- sprintf("renv_%s%s", version, ext) + url <- paste(baseurl, name, sep = "/") + + destfile <- file.path(tempdir(), name) + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (inherits(status, "condition")) + return(FALSE) + + # report success and return + destfile + + } + + renv_bootstrap_download_cran_latest_find <- function(version) { + + # check whether binaries are supported on this system + binary <- + getOption("renv.bootstrap.binary", default = TRUE) && + !identical(.Platform$pkgType, "source") && + !identical(getOption("pkgType"), "source") && + Sys.info()[["sysname"]] %in% c("Darwin", "Windows") + + types <- c(if (binary) "binary", "source") + + # iterate over types + repositories + for (type in types) { + for (repos in renv_bootstrap_repos()) { + + # build arguments for utils::available.packages() call + args <- list(type = type, repos = repos) + + # add custom headers if available -- note that + # utils::available.packages() will pass this to download.file() + if ("headers" %in% names(formals(utils::download.file))) { + headers <- renv_bootstrap_download_custom_headers(repos) + if (length(headers) && is.character(headers)) + args$headers <- headers + } + + # retrieve package database + db <- tryCatch( + as.data.frame( + do.call(utils::available.packages, args), + stringsAsFactors = FALSE + ), + error = identity + ) + + if (inherits(db, "error")) + next + + # check for compatible entry + entry <- db[db$Package %in% "renv" & db$Version %in% version, ] + if (nrow(entry) == 0) + next + + # found it; return spec to caller + spec <- list(entry = entry, type = type, repos = repos) + return(spec) + + } + } + + # if we got here, we failed to find renv + fmt <- "renv %s is not available from your declared package repositories" + stop(sprintf(fmt, version)) + + } + + renv_bootstrap_download_cran_archive <- function(version) { + + name <- sprintf("renv_%s.tar.gz", version) + repos <- renv_bootstrap_repos() + urls <- file.path(repos, "src/contrib/Archive/renv", name) + destfile <- file.path(tempdir(), name) + + for (url in urls) { + + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (identical(status, 0L)) + return(destfile) + + } + + return(FALSE) + + } + + renv_bootstrap_download_tarball <- function(version) { + + # if the user has provided the path to a tarball via + # an environment variable, then use it + tarball <- Sys.getenv("RENV_BOOTSTRAP_TARBALL", unset = NA) + if (is.na(tarball)) + return() + + # allow directories + if (dir.exists(tarball)) { + name <- sprintf("renv_%s.tar.gz", version) + tarball <- file.path(tarball, name) + } + + # bail if it doesn't exist + if (!file.exists(tarball)) { + + # let the user know we weren't able to honour their request + fmt <- "- RENV_BOOTSTRAP_TARBALL is set (%s) but does not exist." + msg <- sprintf(fmt, tarball) + warning(msg) + + # bail + return() + + } + + catf("- Using local tarball '%s'.", tarball) + tarball + + } + + renv_bootstrap_github_token <- function() { + for (envvar in c("GITHUB_TOKEN", "GITHUB_PAT", "GH_TOKEN")) { + envval <- Sys.getenv(envvar, unset = NA) + if (!is.na(envval)) + return(envval) + } + } + + renv_bootstrap_download_github <- function(version) { + + enabled <- Sys.getenv("RENV_BOOTSTRAP_FROM_GITHUB", unset = "TRUE") + if (!identical(enabled, "TRUE")) + return(FALSE) + + # prepare download options + token <- renv_bootstrap_github_token() + if (is.null(token)) + token <- "" + + if (nzchar(Sys.which("curl")) && nzchar(token)) { + fmt <- "--location --fail --header \"Authorization: token %s\"" + extra <- sprintf(fmt, token) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "curl", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } else if (nzchar(Sys.which("wget")) && nzchar(token)) { + fmt <- "--header=\"Authorization: token %s\"" + extra <- sprintf(fmt, token) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "wget", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } + + url <- file.path("https://api.github.com/repos/rstudio/renv/tarball", version) + name <- sprintf("renv_%s.tar.gz", version) + destfile <- file.path(tempdir(), name) + + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (!identical(status, 0L)) + return(FALSE) + + renv_bootstrap_download_augment(destfile) + + return(destfile) + + } + + # Add Sha to DESCRIPTION. This is stop gap until #890, after which we + # can use renv::install() to fully capture metadata. + renv_bootstrap_download_augment <- function(destfile) { + sha <- renv_bootstrap_git_extract_sha1_tar(destfile) + if (is.null(sha)) { + return() + } + + # Untar + tempdir <- tempfile("renv-github-") + on.exit(unlink(tempdir, recursive = TRUE), add = TRUE) + untar(destfile, exdir = tempdir) + pkgdir <- dir(tempdir, full.names = TRUE)[[1]] + + # Modify description + desc_path <- file.path(pkgdir, "DESCRIPTION") + desc_lines <- readLines(desc_path) + remotes_fields <- c( + "RemoteType: github", + "RemoteHost: api.github.com", + "RemoteRepo: renv", + "RemoteUsername: rstudio", + "RemotePkgRef: rstudio/renv", + paste("RemoteRef: ", sha), + paste("RemoteSha: ", sha) + ) + writeLines(c(desc_lines[desc_lines != ""], remotes_fields), con = desc_path) + + # Re-tar + local({ + old <- setwd(tempdir) + on.exit(setwd(old), add = TRUE) + + tar(destfile, compression = "gzip") + }) + invisible() + } + + # Extract the commit hash from a git archive. Git archives include the SHA1 + # hash as the comment field of the tarball pax extended header + # (see https://www.kernel.org/pub/software/scm/git/docs/git-archive.html) + # For GitHub archives this should be the first header after the default one + # (512 byte) header. + renv_bootstrap_git_extract_sha1_tar <- function(bundle) { + + # open the bundle for reading + # We use gzcon for everything because (from ?gzcon) + # > Reading from a connection which does not supply a 'gzip' magic + # > header is equivalent to reading from the original connection + conn <- gzcon(file(bundle, open = "rb", raw = TRUE)) + on.exit(close(conn)) + + # The default pax header is 512 bytes long and the first pax extended header + # with the comment should be 51 bytes long + # `52 comment=` (11 chars) + 40 byte SHA1 hash + len <- 0x200 + 0x33 + res <- rawToChar(readBin(conn, "raw", n = len)[0x201:len]) + + if (grepl("^52 comment=", res)) { + sub("52 comment=", "", res) + } else { + NULL + } + } + + renv_bootstrap_install <- function(version, tarball, library) { + + # attempt to install it into project library + dir.create(library, showWarnings = FALSE, recursive = TRUE) + output <- renv_bootstrap_install_impl(library, tarball) + + # check for successful install + status <- attr(output, "status") + if (is.null(status) || identical(status, 0L)) + return(status) + + # an error occurred; report it + header <- "installation of renv failed" + lines <- paste(rep.int("=", nchar(header)), collapse = "") + text <- paste(c(header, lines, output), collapse = "\n") + stop(text) + + } + + renv_bootstrap_install_impl <- function(library, tarball) { + + # invoke using system2 so we can capture and report output + bin <- R.home("bin") + exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R" + R <- file.path(bin, exe) + + args <- c( + "--vanilla", "CMD", "INSTALL", "--no-multiarch", + "-l", shQuote(path.expand(library)), + shQuote(path.expand(tarball)) + ) + + system2(R, args, stdout = TRUE, stderr = TRUE) + + } + + renv_bootstrap_platform_prefix_default <- function() { + + # read version component + version <- Sys.getenv("RENV_PATHS_VERSION", unset = "R-%v") + + # expand placeholders + placeholders <- list( + list("%v", format(getRversion()[1, 1:2])), + list("%V", format(getRversion()[1, 1:3])) + ) + + for (placeholder in placeholders) + version <- gsub(placeholder[[1L]], placeholder[[2L]], version, fixed = TRUE) + + # include SVN revision for development versions of R + # (to avoid sharing platform-specific artefacts with released versions of R) + devel <- + identical(R.version[["status"]], "Under development (unstable)") || + identical(R.version[["nickname"]], "Unsuffered Consequences") + + if (devel) + version <- paste(version, R.version[["svn rev"]], sep = "-r") + + version + + } + + renv_bootstrap_platform_prefix <- function() { + + # construct version prefix + version <- renv_bootstrap_platform_prefix_default() + + # build list of path components + components <- c(version, R.version$platform) + + # include prefix if provided by user + prefix <- renv_bootstrap_platform_prefix_impl() + if (!is.na(prefix) && nzchar(prefix)) + components <- c(prefix, components) + + # build prefix + paste(components, collapse = "/") + + } + + renv_bootstrap_platform_prefix_impl <- function() { + + # if an explicit prefix has been supplied, use it + prefix <- Sys.getenv("RENV_PATHS_PREFIX", unset = NA) + if (!is.na(prefix)) + return(prefix) + + # if the user has requested an automatic prefix, generate it + auto <- Sys.getenv("RENV_PATHS_PREFIX_AUTO", unset = NA) + if (is.na(auto) && getRversion() >= "4.4.0") + auto <- "TRUE" + + if (auto %in% c("TRUE", "True", "true", "1")) + return(renv_bootstrap_platform_prefix_auto()) + + # empty string on failure + "" + + } + + renv_bootstrap_platform_prefix_auto <- function() { + + prefix <- tryCatch(renv_bootstrap_platform_os(), error = identity) + if (inherits(prefix, "error") || prefix %in% "unknown") { + + msg <- paste( + "failed to infer current operating system", + "please file a bug report at https://github.com/rstudio/renv/issues", + sep = "; " + ) + + warning(msg) + + } + + prefix + + } + + renv_bootstrap_platform_os <- function() { + + sysinfo <- Sys.info() + sysname <- sysinfo[["sysname"]] + + # handle Windows + macOS up front + if (sysname == "Windows") + return("windows") + else if (sysname == "Darwin") + return("macos") + + # check for os-release files + for (file in c("/etc/os-release", "/usr/lib/os-release")) + if (file.exists(file)) + return(renv_bootstrap_platform_os_via_os_release(file, sysinfo)) + + # check for redhat-release files + if (file.exists("/etc/redhat-release")) + return(renv_bootstrap_platform_os_via_redhat_release()) + + "unknown" + + } + + renv_bootstrap_platform_os_via_os_release <- function(file, sysinfo) { + + # read /etc/os-release + release <- utils::read.table( + file = file, + sep = "=", + quote = c("\"", "'"), + col.names = c("Key", "Value"), + comment.char = "#", + stringsAsFactors = FALSE + ) + + vars <- as.list(release$Value) + names(vars) <- release$Key + + # get os name + os <- tolower(sysinfo[["sysname"]]) + + # read id + id <- "unknown" + for (field in c("ID", "ID_LIKE")) { + if (field %in% names(vars) && nzchar(vars[[field]])) { + id <- vars[[field]] + break + } + } + + # read version + version <- "unknown" + for (field in c("UBUNTU_CODENAME", "VERSION_CODENAME", "VERSION_ID", "BUILD_ID")) { + if (field %in% names(vars) && nzchar(vars[[field]])) { + version <- vars[[field]] + break + } + } + + # join together + paste(c(os, id, version), collapse = "-") + + } + + renv_bootstrap_platform_os_via_redhat_release <- function() { + + # read /etc/redhat-release + contents <- readLines("/etc/redhat-release", warn = FALSE) + + # infer id + id <- if (grepl("centos", contents, ignore.case = TRUE)) + "centos" + else if (grepl("redhat", contents, ignore.case = TRUE)) + "redhat" + else + "unknown" + + # try to find a version component (very hacky) + version <- "unknown" + + parts <- strsplit(contents, "[[:space:]]")[[1L]] + for (part in parts) { + + nv <- tryCatch(numeric_version(part), error = identity) + if (inherits(nv, "error")) + next + + version <- nv[1, 1] + break + + } + + paste(c("linux", id, version), collapse = "-") + + } + + renv_bootstrap_library_root_name <- function(project) { + + # use project name as-is if requested + asis <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT_ASIS", unset = "FALSE") + if (asis) + return(basename(project)) + + # otherwise, disambiguate based on project's path + id <- substring(renv_bootstrap_hash_text(project), 1L, 8L) + paste(basename(project), id, sep = "-") + + } + + renv_bootstrap_library_root <- function(project) { + + prefix <- renv_bootstrap_profile_prefix() + + path <- Sys.getenv("RENV_PATHS_LIBRARY", unset = NA) + if (!is.na(path)) + return(paste(c(path, prefix), collapse = "/")) + + path <- renv_bootstrap_library_root_impl(project) + if (!is.null(path)) { + name <- renv_bootstrap_library_root_name(project) + return(paste(c(path, prefix, name), collapse = "/")) + } + + renv_bootstrap_paths_renv("library", project = project) + + } + + renv_bootstrap_library_root_impl <- function(project) { + + root <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT", unset = NA) + if (!is.na(root)) + return(root) + + type <- renv_bootstrap_project_type(project) + if (identical(type, "package")) { + userdir <- renv_bootstrap_user_dir() + return(file.path(userdir, "library")) + } + + } + + renv_bootstrap_validate_version <- function(version, description = NULL) { + + # resolve description file + # + # avoid passing lib.loc to `packageDescription()` below, since R will + # use the loaded version of the package by default anyhow. note that + # this function should only be called after 'renv' is loaded + # https://github.com/rstudio/renv/issues/1625 + description <- description %||% packageDescription("renv") + + # check whether requested version 'version' matches loaded version of renv + sha <- attr(version, "sha", exact = TRUE) + valid <- if (!is.null(sha)) + renv_bootstrap_validate_version_dev(sha, description) + else + renv_bootstrap_validate_version_release(version, description) + + if (valid) + return(TRUE) + + # the loaded version of renv doesn't match the requested version; + # give the user instructions on how to proceed + dev <- identical(description[["RemoteType"]], "github") + remote <- if (dev) + paste("rstudio/renv", description[["RemoteSha"]], sep = "@") + else + paste("renv", description[["Version"]], sep = "@") + + # display both loaded version + sha if available + friendly <- renv_bootstrap_version_friendly( + version = description[["Version"]], + sha = if (dev) description[["RemoteSha"]] + ) + + fmt <- heredoc(" + renv %1$s was loaded from project library, but this project is configured to use renv %2$s. + - Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile. + - Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library. + ") + catf(fmt, friendly, renv_bootstrap_version_friendly(version), remote) + + FALSE + + } + + renv_bootstrap_validate_version_dev <- function(version, description) { + + expected <- description[["RemoteSha"]] + if (!is.character(expected)) + return(FALSE) + + pattern <- sprintf("^\\Q%s\\E", version) + grepl(pattern, expected, perl = TRUE) + + } + + renv_bootstrap_validate_version_release <- function(version, description) { + expected <- description[["Version"]] + is.character(expected) && identical(expected, version) + } + + renv_bootstrap_hash_text <- function(text) { + + hashfile <- tempfile("renv-hash-") + on.exit(unlink(hashfile), add = TRUE) + + writeLines(text, con = hashfile) + tools::md5sum(hashfile) + + } + + renv_bootstrap_load <- function(project, libpath, version) { + + # try to load renv from the project library + if (!requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) + return(FALSE) + + # warn if the version of renv loaded does not match + renv_bootstrap_validate_version(version) + + # execute renv load hooks, if any + hooks <- getHook("renv::autoload") + for (hook in hooks) + if (is.function(hook)) + tryCatch(hook(), error = warnify) + + # load the project + renv::load(project) + + TRUE + + } + + renv_bootstrap_profile_load <- function(project) { + + # if RENV_PROFILE is already set, just use that + profile <- Sys.getenv("RENV_PROFILE", unset = NA) + if (!is.na(profile) && nzchar(profile)) + return(profile) + + # check for a profile file (nothing to do if it doesn't exist) + path <- renv_bootstrap_paths_renv("profile", profile = FALSE, project = project) + if (!file.exists(path)) + return(NULL) + + # read the profile, and set it if it exists + contents <- readLines(path, warn = FALSE) + if (length(contents) == 0L) + return(NULL) + + # set RENV_PROFILE + profile <- contents[[1L]] + if (!profile %in% c("", "default")) + Sys.setenv(RENV_PROFILE = profile) + + profile + + } + + renv_bootstrap_profile_prefix <- function() { + profile <- renv_bootstrap_profile_get() + if (!is.null(profile)) + return(file.path("profiles", profile, "renv")) + } + + renv_bootstrap_profile_get <- function() { + profile <- Sys.getenv("RENV_PROFILE", unset = "") + renv_bootstrap_profile_normalize(profile) + } + + renv_bootstrap_profile_set <- function(profile) { + profile <- renv_bootstrap_profile_normalize(profile) + if (is.null(profile)) + Sys.unsetenv("RENV_PROFILE") + else + Sys.setenv(RENV_PROFILE = profile) + } + + renv_bootstrap_profile_normalize <- function(profile) { + + if (is.null(profile) || profile %in% c("", "default")) + return(NULL) + + profile + + } + + renv_bootstrap_path_absolute <- function(path) { + + substr(path, 1L, 1L) %in% c("~", "/", "\\") || ( + substr(path, 1L, 1L) %in% c(letters, LETTERS) && + substr(path, 2L, 3L) %in% c(":/", ":\\") + ) + + } + + renv_bootstrap_paths_renv <- function(..., profile = TRUE, project = NULL) { + renv <- Sys.getenv("RENV_PATHS_RENV", unset = "renv") + root <- if (renv_bootstrap_path_absolute(renv)) NULL else project + prefix <- if (profile) renv_bootstrap_profile_prefix() + components <- c(root, renv, prefix, ...) + paste(components, collapse = "/") + } + + renv_bootstrap_project_type <- function(path) { + + descpath <- file.path(path, "DESCRIPTION") + if (!file.exists(descpath)) + return("unknown") + + desc <- tryCatch( + read.dcf(descpath, all = TRUE), + error = identity + ) + + if (inherits(desc, "error")) + return("unknown") + + type <- desc$Type + if (!is.null(type)) + return(tolower(type)) + + package <- desc$Package + if (!is.null(package)) + return("package") + + "unknown" + + } + + renv_bootstrap_user_dir <- function() { + dir <- renv_bootstrap_user_dir_impl() + path.expand(chartr("\\", "/", dir)) + } + + renv_bootstrap_user_dir_impl <- function() { + + # use local override if set + override <- getOption("renv.userdir.override") + if (!is.null(override)) + return(override) + + # use R_user_dir if available + tools <- asNamespace("tools") + if (is.function(tools$R_user_dir)) + return(tools$R_user_dir("renv", "cache")) + + # try using our own backfill for older versions of R + envvars <- c("R_USER_CACHE_DIR", "XDG_CACHE_HOME") + for (envvar in envvars) { + root <- Sys.getenv(envvar, unset = NA) + if (!is.na(root)) + return(file.path(root, "R/renv")) + } + + # use platform-specific default fallbacks + if (Sys.info()[["sysname"]] == "Windows") + file.path(Sys.getenv("LOCALAPPDATA"), "R/cache/R/renv") + else if (Sys.info()[["sysname"]] == "Darwin") + "~/Library/Caches/org.R-project.R/R/renv" + else + "~/.cache/R/renv" + + } + + renv_bootstrap_version_friendly <- function(version, shafmt = NULL, sha = NULL) { + sha <- sha %||% attr(version, "sha", exact = TRUE) + parts <- c(version, sprintf(shafmt %||% " [sha: %s]", substring(sha, 1L, 7L))) + paste(parts, collapse = "") + } + + renv_bootstrap_exec <- function(project, libpath, version) { + if (!renv_bootstrap_load(project, libpath, version)) + renv_bootstrap_run(project, libpath, version) + } + + renv_bootstrap_run <- function(project, libpath, version) { + + # perform bootstrap + bootstrap(version, libpath) + + # exit early if we're just testing bootstrap + if (!is.na(Sys.getenv("RENV_BOOTSTRAP_INSTALL_ONLY", unset = NA))) + return(TRUE) + + # try again to load + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + return(renv::load(project = project)) + } + + # failed to download or load renv; warn the user + msg <- c( + "Failed to find an renv installation: the project will not be loaded.", + "Use `renv::activate()` to re-initialize the project." + ) + + warning(paste(msg, collapse = "\n"), call. = FALSE) + + } + + renv_json_read <- function(file = NULL, text = NULL) { + + jlerr <- NULL + + # if jsonlite is loaded, use that instead + if ("jsonlite" %in% loadedNamespaces()) { + + json <- tryCatch(renv_json_read_jsonlite(file, text), error = identity) + if (!inherits(json, "error")) + return(json) + + jlerr <- json + + } + + # otherwise, fall back to the default JSON reader + json <- tryCatch(renv_json_read_default(file, text), error = identity) + if (!inherits(json, "error")) + return(json) + + # report an error + if (!is.null(jlerr)) + stop(jlerr) + else + stop(json) + + } + + renv_json_read_jsonlite <- function(file = NULL, text = NULL) { + text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + jsonlite::fromJSON(txt = text, simplifyVector = FALSE) + } + + renv_json_read_patterns <- function() { + + list( + + # objects + list("{", "\t\n\tobject(\t\n\t", TRUE), + list("}", "\t\n\t)\t\n\t", TRUE), + + # arrays + list("[", "\t\n\tarray(\t\n\t", TRUE), + list("]", "\n\t\n)\n\t\n", TRUE), + + # maps + list(":", "\t\n\t=\t\n\t", TRUE), + + # newlines + list("\\u000a", "\n", FALSE) + + ) + + } + + renv_json_read_envir <- function() { + + envir <- new.env(parent = emptyenv()) + + envir[["+"]] <- `+` + envir[["-"]] <- `-` + + envir[["object"]] <- function(...) { + result <- list(...) + names(result) <- as.character(names(result)) + result + } + + envir[["array"]] <- list + + envir[["true"]] <- TRUE + envir[["false"]] <- FALSE + envir[["null"]] <- NULL + + envir + + } + + renv_json_read_remap <- function(object, patterns) { + + # repair names if necessary + if (!is.null(names(object))) { + + nms <- names(object) + for (pattern in patterns) + nms <- gsub(pattern[[2L]], pattern[[1L]], nms, fixed = TRUE) + names(object) <- nms + + } + + # repair strings if necessary + if (is.character(object)) { + for (pattern in patterns) + object <- gsub(pattern[[2L]], pattern[[1L]], object, fixed = TRUE) + } + + # recurse for other objects + if (is.recursive(object)) + for (i in seq_along(object)) + object[i] <- list(renv_json_read_remap(object[[i]], patterns)) + + # return remapped object + object + + } + + renv_json_read_default <- function(file = NULL, text = NULL) { + + # read json text + text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + + # convert into something the R parser will understand + patterns <- renv_json_read_patterns() + transformed <- text + for (pattern in patterns) + transformed <- gsub(pattern[[1L]], pattern[[2L]], transformed, fixed = TRUE) + + # parse it + rfile <- tempfile("renv-json-", fileext = ".R") + on.exit(unlink(rfile), add = TRUE) + writeLines(transformed, con = rfile) + json <- parse(rfile, keep.source = FALSE, srcfile = NULL)[[1L]] + + # evaluate in safe environment + result <- eval(json, envir = renv_json_read_envir()) + + # fix up strings if necessary -- do so only with reversible patterns + patterns <- Filter(function(pattern) pattern[[3L]], patterns) + renv_json_read_remap(result, patterns) + + } + + + # load the renv profile, if any + renv_bootstrap_profile_load(project) + + # construct path to library root + root <- renv_bootstrap_library_root(project) + + # construct library prefix for platform + prefix <- renv_bootstrap_platform_prefix() + + # construct full libpath + libpath <- file.path(root, prefix) + + # run bootstrap code + renv_bootstrap_exec(project, libpath, version) + + invisible() + +}) diff --git a/renv/settings.json b/renv/settings.json new file mode 100644 index 0000000..ffdbb32 --- /dev/null +++ b/renv/settings.json @@ -0,0 +1,19 @@ +{ + "bioconductor.version": null, + "external.libraries": [], + "ignored.packages": [], + "package.dependency.fields": [ + "Imports", + "Depends", + "LinkingTo" + ], + "ppm.enabled": null, + "ppm.ignored.urls": [], + "r.version": null, + "snapshot.type": "implicit", + "use.cache": true, + "vcs.ignore.cellar": true, + "vcs.ignore.library": true, + "vcs.ignore.local": true, + "vcs.manage.ignores": true +} diff --git a/reports/figures/fig_final_conf_mat.png b/reports/figures/fig_final_conf_mat.png new file mode 100644 index 0000000..100e9d9 Binary files /dev/null and b/reports/figures/fig_final_conf_mat.png differ diff --git a/reports/figures/fig_final_curves.png b/reports/figures/fig_final_curves.png new file mode 100644 index 0000000..56c7459 Binary files /dev/null and b/reports/figures/fig_final_curves.png differ diff --git a/reports/figures/fig_fraud_by_month.png b/reports/figures/fig_fraud_by_month.png new file mode 100644 index 0000000..7904517 Binary files /dev/null and b/reports/figures/fig_fraud_by_month.png differ diff --git a/reports/figures/fig_hexbin_interaction.png b/reports/figures/fig_hexbin_interaction.png new file mode 100644 index 0000000..b7d2c54 Binary files /dev/null and b/reports/figures/fig_hexbin_interaction.png differ diff --git a/reports/figures/fig_missingness.png b/reports/figures/fig_missingness.png new file mode 100644 index 0000000..873c22d Binary files /dev/null and b/reports/figures/fig_missingness.png differ diff --git a/reports/figures/fig_num_cor.png b/reports/figures/fig_num_cor.png new file mode 100644 index 0000000..7d4ca5f Binary files /dev/null and b/reports/figures/fig_num_cor.png differ diff --git a/reports/figures/fig_strategy_showdown.png b/reports/figures/fig_strategy_showdown.png new file mode 100644 index 0000000..798de62 Binary files /dev/null and b/reports/figures/fig_strategy_showdown.png differ diff --git a/reports/figures/fig_var_imp.png b/reports/figures/fig_var_imp.png new file mode 100644 index 0000000..4170bfe Binary files /dev/null and b/reports/figures/fig_var_imp.png differ diff --git a/resources/images/confusion-matrix.png b/resources/images/confusion-matrix.png new file mode 100644 index 0000000..0c533ac Binary files /dev/null and b/resources/images/confusion-matrix.png differ