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SelectBoost.gamlss 0.2.2

• Code and description fixes requested by CRAN.

SelectBoost.gamlss 0.2.1

• Additional fixes to code and documentation to silence CRAN check notes for R devel.
• Added the boys7482 dataset.
• Fix knockoff filters to coerce grouped design matrices to numeric and supply the response to knockoff::create.fixed(), preventing failures when smooth proxies carried non-numeric classes or the design needed augmentation, and reuse any augmented design/response returned from create.fixed() to avoid downstream dimension mismatches.

SelectBoost.gamlss 0.2.0

Highlights

• Grouped selection for all parameters (μ, σ, ν, τ) with engine = "grpreg" (group lasso/MCP/SCAD) and engine = "sgl" (sparse group lasso), including factors, splines (pb()/cs()), and interactions treated as single groups.
• Per-parameter engines: engine, engine_sigma, engine_nu, engine_tau can be mixed (stepwise / glmnet / grpreg / sgl).
• Glmnet support (lasso/ridge/elastic-net) extended beyond μ via working-response proxies for σ/ν/τ.
• Glmnet selectors now accept glmnet_family (gaussian/binomial/poisson) and handle factor predictors via model-matrix expansion.
• Tuning framework: tune_sb_gamlss() with stability or deviance metrics (K-fold), progress bars, and a complexity penalty.
• Fast deviance paths for common families (auto-used in deviance CV): NO, PO, LOGNO, GA, IG, LO, LOGITNO, GEOM, BE, NBI, NBII, BI, and native shortcuts via gamlss.dist for many others (e.g., LOGLOG, DEL, ZAGA, ZIP/ZIP2, ZAIG, ZALG, ZIBI/ZIBB, PARETO, SEP1/SEP2, ZIPF/ZIPFmu, BCT, BCPE, SICHEL, GLG, BETA4, RS, WEI, GIG), with graceful fallbacks.
• Fast deviance dynamically calls gamlss.dist::d<family>() when available, broadening zero-inflated/hurdle coverage without manual whitelists.
• Group knockoffs (approximate) for FDR-style control: knockoff_filter_mu(), knockoff_filter_param().
  • Robust to rows dropped by model.matrix() (e.g., missing predictors) by aligning the response / working response before building knockoffs.
• Compiled speedups (Rcpp/RcppArmadillo) for scaling/cor; parallel bootstraps via future.apply.

New user-facing functions

• tune_sb_gamlss(), knockoff_filter_mu(), knockoff_filter_param()
• fast_vs_generic_ll(), check_fast_vs_generic()
• effect_plot() (quick partial effect visualizer for the final selected model)

New arguments in sb_gamlss()

• engine_sigma, engine_nu, engine_tau — choose engines per-parameter
• grpreg_penalty (grLasso/grMCP/grSCAD), sgl_alpha
• df_smooth — basis size for grouped-smoother proxies
• progress — progress bar for sequential bootstraps
• (still) glmnet_alpha — 0=ridge, 1=lasso, (0,1)=EN
• glmnet_family — choose gaussian/binomial/poisson for glmnet selectors

Documentation & vignettes

• Real Data Examples (including growth/BCT on gamlss.data::boys)
• Advanced Real Data Examples (ZIP/ZINB on bioChemists, ZAGA on airquality::Ozone, longitudinal growth on nlme::Orthodont with random intercepts)
• Benchmarks (engine timings) and Fast deviance microbenchmarks
• Fast deviance equality (accuracy checks) + wide-family sweep with per-family tolerances & skip reasons
• pkgdown site scaffolding + GitHub Actions workflow
• README quick start now covers factor effects, BCT four-parameter example, and deviance-based tuning metrics.

Testing & quality

• Unit tests for fast vs generic deviance (accuracy and presence of native densities)
• Opt-in long tests via options(SelectBoost.gamlss.run_long_tests=TRUE) or RUN_LONG_TESTS=true

Notes

• Some grouped/knockoff features are optional and require packages in Suggests (grpreg, SGL, knockoff, glmnet, etc.).
• Smooths are proxied with splines::bs(df = df_smooth) for selection only; the final gamlss fit remains as specified.

SelectBoost.gamlss 0.1.0

• First draft: bootstrap stability-selection over GAMLSS parameters (mu/sigma/nu/tau).
• Optional pre-standardization of numeric predictors (stored for prediction).
• AICc helper.
• Plotting + prediction helpers.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.