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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’.

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.