NEWS
causalreg 0.3.0 (2026-10-01)
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.