evandeilton.r-universe.dev

[evandeilton] gkwdist 1.1.4

evandeilton@gmail.com (José Evandeilton Lopes) — Wed, 27 May 2026 19:34:46 GMT

Implements the five-parameter Generalized Kumaraswamy ('gkw') distribution proposed by 'Carrasco, Ferrari and Cordeiro (2010)' 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) . 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.

[evandeilton] betaregscale 2.7.1

evandeilton@gmail.com (José Evandeilton Lopes) — Sat, 23 May 2026 19:07:39 GMT

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.

[evandeilton] OptimalBinningWoE 1.10.0

evandeilton@gmail.com (José Evandeilton Lopes) — Wed, 20 May 2026 22:18:03 GMT

High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) and Navas-Palencia (2020) .

[evandeilton] gkwreg 2.1.16

evandeilton@gmail.com (José Evandeilton Lopes) — Sat, 21 Mar 2026 03:25:25 GMT

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.