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OptimalBinningWoE - Optimal Binning and Weight of Evidence Framework for Modeling

High-performance implementation of 28 binning algorithms (37 implementations: 21 numerical, 16 categorical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Methods include dynamic programming over ordered levels, entropy-based ('MDLP') and chi-square ('ChiMerge') partitioning, isotonic regression, simulated annealing, kernel-density binning, sketch-based binning (KLL and Count-Min), and greedy merging heuristics. Features a default algorithm chosen from the target type, monotonicity enforcement where the algorithm supports it, and efficient handling of large datasets via 'Rcpp'. Provides automated variable screening by Information Value strength and bin ordering, and generation of the equivalent 'SQL' 'CASE' expressions for in-database scoring. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) <doi:10.1002/9781119201731> and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>.

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binningcredit-scoringfeature-engineeringmodelingrisksupervised-learningwoecppopenmp

6.63 score 4 stars 1 dependents 14 scripts 554 downloads

gkwdist - Generalized Kumaraswamy Distribution Family

Implements the five-parameter Generalized Kumaraswamy ('gkw') distribution proposed by 'Carrasco, Ferrari and Cordeiro (2010)' <doi:10.48550/arXiv.1004.0911> and its seven nested sub-families for modeling bounded continuous data on the unit interval (0,1). The 'gkw' distribution extends the Kumaraswamy distribution described by Jones (2009) <doi:10.1016/j.stamet.2008.04.001>. Provides density, distribution, quantile, and random generation functions, along with analytical log-likelihood, gradient, and Hessian functions implemented in 'C++' via 'RcppArmadillo' for maximum computational efficiency. Suitable for modeling proportions, rates, percentages, and indices exhibiting complex features such as asymmetry, or heavy tails and other shapes not adequately captured by standard distributions like simple Beta or Kumaraswamy.

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cppdistributiongkwrcpparmadillounit-intervalcpp

5.65 score 1 stars 1 dependents 406 downloads

betaregscale - Beta Regression for Interval-Censored Scale-Derived Outcomes

Maximum-likelihood estimation of beta regression models for responses derived from bounded rating scales. Observations are treated as interval-censored on (0, 1) after a scale-to-unit transformation, and the likelihood is built from the difference of the beta CDF at the interval endpoints. The complete likelihood supports mixed censoring types: uncensored, left-censored, right-censored, and interval-censored observations. Both fixed- and variable-dispersion submodels are supported, with flexible link functions for the mean and precision components. A compiled C++ backend (via 'Rcpp' and 'RcppArmadillo') provides numerically stable, high-performance log-likelihood evaluation. Standard S3 methods (print(), summary(), coef(), fitted(), residuals(), predict(), plot(), confint(), vcov(), logLik(), AIC(), BIC()) are available for fitted objects.

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beta-mappingbeta-regressionnrs-101nrs-11nrs21openblascpp

5.28 score 1 stars 12 scripts 373 downloads

gkwreg - Generalized Kumaraswamy Regression Models for Bounded Data

Implements regression models for bounded continuous data in the open interval (0,1) using the five-parameter Generalized 'Kumaraswamy' distribution. Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models. Methods are described in Lopes and Bonat (2026) <doi:10.21105/joss.08991>.

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gkwregkumaraswamy-regressionrcpptmbunit-intervalcpp

5.15 score 2 stars 575 downloads

scorecraft - Scorecard Development and Internal Ratings-Based Risk Parameters

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>.

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openblascppopenmp

4.90 score 265 downloads