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causalreg 0.3.0

• cglm() and cgam() gain a direction argument for the stepwise search, "forward" (the default and the previous behaviour) or "backward". The backward search starts from the full model and removes, at each step, the variable whose removal gives the largest Pearson-risk p-value, stopping once the model is no longer rejected. Both directions then prune by BIC as before. Forward remains the default because it is cheaper.
• For a binomial response whose candidate set contains a categorical variable, the stepwise search now runs backward and says so. The Pearson risk of a binary regression on categorical covariates alone is mathematically equal to 1, so a forward search can enter such a variable at the first step and stop there.
• The stepwise search now emits the same “only one categorical variable” message that the exhaustive search already emitted.
• The backward search reports "no potential causal model found" when no model on its path was accepted, matching search = "all". Previously it returned the single-term model it happened to end on, which the test had rejected.

causalreg 0.2.2

• Parallel model search (ncores > 1) now works on all platforms. On Unix/macOS it continues to use forking (parallel::mclapply); on Windows it uses a PSOCK cluster (parallel::parLapply) with parallel-safe RNG streams. Previously ncores was silently ignored on Windows.
• Added the causalreg.parallel option to select the parallel backend ("auto", "fork", "psock", "sequential").

causalreg 0.2.1

• cgam() fast bootstrap via fixed smoothing parameters (fast_gam = TRUE, the default): the smoothing parameters selected on the original data are held fixed across that model’s bootstrap resamples, giving a 3-4x speedup.

causalreg 0.2.0

• Added C++/Rcpp acceleration for GLM fitting, Pearson statistics, and the bootstrap (use_cpp = TRUE, the default for supported families).
• Added parallel model evaluation via the ncores argument.

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.