Package: scorecraft
Title: Scorecard Development and Internal Ratings-Based Risk Parameters
Version: 0.3.0
Authors@R: c(
    person("Jose Evandeilton", "Lopes", email = "evandeilton@gmail.com",
           role = c("aut", "cre", "cph"))
  )
Description: Builds points scorecards for binary targets (credit risk, fraud,
    propensity) on the optimal binning and weight of evidence engine of
    'OptimalBinningWoE', and takes them to the risk parameters of the internal
    ratings-based (IRB) approach. Variables are selected through optimal
    binning, eight admission rules, hold-out revalidation with frozen bins and
    a consensus of 'glmnet', 'xgboost', 'lightgbm' and 'ranger' models weighted
    by out-of-sample performance; the audit funnel never drops a candidate
    from the report. The scorecard is fitted with an explicit, auditable scale
    alignment (a log-odds regression on the raw score composed with the
    points-to-double-the-odds map); cut-offs are swept with frozen cuts;
    reject inference is reported as a sensitivity band; the population and
    characteristic stability indices (PSI and CSI) are monitored with both the
    fixed and the sample-size-adjusted threshold; and production SQL is
    generated in fourteen dialects, with the agreement between R and SQL
    verified by test. The IRB layer builds the default flag; calibrates the
    scorecard to a long-run default rate with rating grades, margins of
    conservatism and floors to give the probability of default (PD); models
    workout loss given default (LGD) in two stages with downturn and
    in-default estimates; models credit conversion factors from facility
    snapshots to give the exposure at default (EAD); and computes expected
    loss, risk weights, regulatory capital and expected credit loss from
    parameter tables selected by framework preset. The heavy numeric kernels
    (rank correlation of wide weight of evidence tables, exact concordance
    counts for Somers' D, streamed expected credit loss paths) are compiled
    with 'RcppArmadillo'. The scorecard methodology follows Siddiqi (2017)
    <doi:10.1002/9781119282396> and Thomas et al. (2017)
    <doi:10.1137/1.9781611974560>.
License: MIT + file LICENSE
URL: https://github.com/evandeilton/scorecraft
BugReports: https://github.com/evandeilton/scorecraft/issues
Encoding: UTF-8
Language: en-GB
Depends: R (>= 4.1.0)
Imports: data.table (>= 1.14.0), OptimalBinningWoE (>= 1.13.4),
        xgboost, stats, utils, graphics, parallel, Rcpp (>= 1.0.10)
LinkingTo: Rcpp, RcppArmadillo
Suggests: glmnet, lightgbm, ranger, DBI, odbc, RSQLite, duckdb,
        openxlsx, betareg, bit64, testthat (>= 3.0.0), knitr,
        rmarkdown, withr
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/testthat/parallel: false
Config/roxygen2/version: 8.1.0
NeedsCompilation: yes
Packaged: 2026-09-25 22:47:44 UTC; evandeilton
Author: Jose Evandeilton Lopes [aut, cre, cph]
Maintainer: Jose Evandeilton Lopes <evandeilton@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-06 16:10:02 UTC
