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SelectBoost.gamlss: Stability-Selection via Correlated Resampling for 'GAMLSS'Models

Extends the 'SelectBoost' approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) <https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

Version:	0.2.2
Depends:	R (≥ 4.1.0)
Imports:	gamlss,Rcpp,rlang,SelectBoost, stats, utils
LinkingTo:	Rcpp,RcppArmadillo
Suggests:	doParallel,foreach,future,future.apply,gamlss.data,gamlss.dist,ggplot2,glmnet,grpreg,knitr,knockoff,MASS,microbenchmark,nlme, parallel,pkgdown,pscl,rmarkdown,SGL,testthat (≥ 3.0.0)
Published:	2025-11-25
DOI:	10.32614/CRAN.package.SelectBoost.gamlss
Author:	Frederic Bertrand [cre, aut]
Maintainer:	Frederic Bertrand <frederic.bertrand at lecnam.net>
BugReports:	https://github.com/fbertran/SelectBoost.gamlss/issues
License:	GPL-3
URL:	https://fbertran.github.io/SelectBoost.gamlss/,https://github.com/fbertran/SelectBoost.gamlss
NeedsCompilation:	yes
SystemRequirements:	C++17
Classification/MSC:	62H11,62J12,62J99
Citation:	SelectBoost.gamlss citation info
Materials:	README,NEWS
CRAN checks:	SelectBoost.gamlss results

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