Changes in version 1.0.0 (2026-07-04)                  

Initial CRAN release.

Variable selection

  - Iterated block Gibbs samplers with screen–select–threshold
    refinement (glmIBGS(), coxIBGS(), lmeIBGS()) and matching plain
    block Gibbs samplers (glmGibbs(), coxGibbs(), lmeGibbs()) for
    ultrahigh-dimensional problems.
  - Families: gaussian, binomial and poisson generalized linear models
    (least squares / iteratively reweighted least squares), the Cox
    proportional-hazards model (Efron partial likelihood), and linear
    mixed models.
  - Model selection by AIC, BIC, AICc or extended BIC (exBIC).

Sampler controls

  - start = c("null", "full") sets the initial model of the Gibbs
    chain(s); the default "null" starts from the empty model and grows,
    avoiding the ill-conditioned full-model start.
  - permute = TRUE (the default) draws a fresh random coordinate
    permutation (Fisher–Yates) each Gibbs sweep, so every coordinate is
    updated exactly once per sweep; permute = FALSE restores a fixed
    in-order sweep.
  - glmGibbs()/glmIBGS() accept an opt-in fast = TRUE for the
    binomial/poisson families, scoring each single-coordinate proposal
    with one warm-started IRLS step and re-fitting only accepted models
    to full convergence (reported criteria and coefficients stay exact).
  - Parallel block screening through 'OpenMP' (n.cores) and an optional
    near-collinearity guard (cor.check).

Model averaging

  - Each fit retains the best n.models models, summarized in C so the
    returned object stays compact even for thousands of predictors.
  - predict(), fitted() and coef() average over the retained models with
    smooth-SIC (BMA-style) weights, on the link or response scale; a
    single retained model can be selected with average = FALSE.
    Conditional prediction with random-effect BLUPs is available for lme
    fits.

Object methods and diagnostics

  - print() and summary() for the fit, with selected-variable and
    top-model tables and a convergence-diagnostics block.
  - plot() dispatches to the diagnostics, also exported individually:
    plotICtrace() (criterion trace), plotMargProb() (marginal inclusion
    probabilities as a dot-and-whisker plot), plotModelFreq() (top-model
    visit frequencies), plotGelman() and plotAutocorr(). plotMargProb()
    and plotModelFreq() accept horizontal = TRUE for a horizontal
    layout.
  - Convergence diagnostics computed in C: the Gelman–Rubin
    shrink-factor evolution, the Geweke z-statistic, the effective
    sample size, and the autocorrelation of the criterion trace.

C backend

  - The sampler is implemented in C with parallel block screening
    through 'OpenMP'.