Stability-Selection via Correlated Resampling for 'GAMLSS' Models
AICc for a gamlss fit
AutoBoost for GAMLSS (SelectBoost-style)
Numerical check: fast vs generic deviance log-likelihood
Confidence functionals from a c0 grid
Compute SelectBoost-like confidence table across c0
K-fold deviance for an sb_gamlss configuration
Reasonable defaults
Per-family numeric tolerance for equality checks
Try to generate values for a family
One-variable effect plot from an sb_gamlss (or gamlss) fit
Compare fast vs generic deviance log-likelihood evaluation
FastBoost for GAMLSS (lightweight stability selection)
Get a density function for a gamlss family
Knockoff filter for mu (approximate group control)
Knockoff filter for sigma/nu/tau (approximate group control)
Log-likelihood (sum) on newdata given a gamlss fit
Plot selection frequencies for sb_gamlss
Plot stability curves p(c0) for selected terms
Plot confidence functionals
Plot summary for sb_gamlss_c0_grid
Plot selection proportions for a single sb_gamlss
Predict distribution parameters on newdata
Stability curves over a c0 grid for sb_gamlss
SelectBoost for GAMLSS (stability selection)
SelectBoost-style wrapper for GAMLSS
SelectBoost.gamlss: Stability-Selection via Correlated Resampling for ...
Selection table accessor
Tune select engines/penalties via a small stability run
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.
Useful links