{ "_id": "6ad16af929625c14e47ae51c", "Package": "scorecraft", "Title": "Scorecard Development and Internal Ratings-Based Risk Parameters", "Version": "0.3.2", "Authors@R": "c(\nperson(\"Jose Evandeilton\", \"Lopes\", email = \"evandeilton@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"))\n)", "Description": "Builds points scorecards for binary targets (credit risk,\nfraud, propensity) on the optimal binning and weight of\nevidence engine of 'OptimalBinningWoE', and takes them to the\nrisk parameters of the internal ratings-based (IRB) approach.\nVariables are selected through optimal binning, eight admission\nrules, hold-out revalidation with frozen bins and a consensus\nof 'glmnet', 'xgboost', 'lightgbm' and 'ranger' models weighted\nby out-of-sample performance; the audit funnel never drops a\ncandidate from the report. The scorecard is fitted with an\nexplicit, auditable scale alignment (a log-odds regression on\nthe raw score composed with the points-to-double-the-odds map);\ncut-offs are swept with frozen cuts; reject inference is\nreported as a sensitivity band; the population and\ncharacteristic stability indices (PSI and CSI) are monitored\nwith both the fixed and the sample-size-adjusted threshold; and\nproduction SQL is generated in fourteen dialects, with the\nagreement between R and SQL verified by test. The IRB layer\nbuilds the default flag; calibrates the scorecard to a long-run\ndefault rate with rating grades, margins of conservatism and\nfloors to give the probability of default (PD); models workout\nloss given default (LGD) in two stages with downturn and\nin-default estimates; models credit conversion factors from\nfacility snapshots to give the exposure at default (EAD); and\ncomputes expected loss, risk weights, regulatory capital and\nexpected credit loss from parameter tables selected by\nframework preset. The heavy numeric kernels (rank correlation\nof wide weight of evidence tables, exact concordance counts for\nSomers' D, streamed expected credit loss paths) are compiled\nwith 'RcppArmadillo'. The scorecard methodology follows Siddiqi\n(2017) and Thomas et al. (2017)\n.", "License": "MIT + file LICENSE", "URL": "https://github.com/evandeilton/scorecraft,\nhttps://evandeilton.github.io/scorecraft/", "BugReports": "https://github.com/evandeilton/scorecraft/issues", "Encoding": "UTF-8", "Language": "en-US", "LazyData": "true", "LazyDataCompression": "xz", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/testthat/parallel": "false", "Roxygen": "list(markdown = TRUE)", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "make libicu-dev", "Repository": "https://evandeilton.r-universe.dev", "Date/Publication": "2026-10-10 22:45:06 UTC", "RemoteUrl": "https://github.com/evandeilton/scorecraft", "RemoteRef": "HEAD", "RemoteSha": "3906bceb52b09cc67ec4389dfb78f71cc7695725", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-15 23:49:22 UTC", "User": "root" }, "Author": "Jose Evandeilton Lopes [aut, cre, cph]", "Maintainer": "Jose Evandeilton Lopes ", "_user": "evandeilton", "_type": "src", "_file": 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