Changes in version 0.2.2 (2025-11-25)                  

  - Code and description fixes requested by CRAN.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.