IBGS 1.0.0

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’.