Stability-Selection via Correlated Resampling for Beta-Regression Models
SelectBoost workflow for interval responses
Simulate interval Beta-regression data (flexible)
Beta regression Elastic-Net via GAMLSS (gamlss.lasso)
Apply a selector to a collection of resampled designs
Pure glmnet IRLS selector for Beta regression
Beta regression LASSO via GAMLSS
Stepwise Beta regression by AIC
Stepwise Beta regression by AICc (finite-sample corrected AIC)
Stepwise Beta regression by BIC
Bootstrap selection frequencies across selectors
Run all selectors once on a dataset
Merge single-run results and bootstrap frequencies
Interval-response stability selection (fastboost variant)
Side-by-side coefficient heatmap
Side-by-side selection-frequency heatmap
User-friendly methods for sb_beta() results
SelectBoost for beta-regression models
Core helpers for SelectBoost-style beta regression
Generate correlated design replicates for a set of groups
Compute selection frequencies from coefficient paths
SelectBoost.beta: Stability-Selection via Correlated Resampling for Be...
Adds variable-selection functions for Beta regression models (both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm. Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector with an optional standardization speedup, and 'C++' helpers for iterative re-weighted least squares working steps and precision updates. Also provides a fastboost_interval() variant for interval responses, comparison helpers, and a flexible simulator simulation_DATA.beta() for interval-valued data. For more details see Bertrand and Maumy (2023) <doi:10.7490/f1000research.1119552.1>.
Useful links