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Major release. The Polya-Gamma sampler and its native C engine,
DIC-based selectModel(), and the identifiability-aware
convergence diagnostics in this release were contributed by Chris
Kypridemos.
method = "pg", now the
default): a joint Polya-Gamma data-augmentation sampler with exact full
conditionals and no Metropolis tuning. Supports overdispersion,
age/period/cohort heterogeneity and period/cohort covariates natively.
The legacy Taylor sampler remains available via
method = "taylor".pg_engine = "C", the default) with an equivalent pure-R
reference (pg_engine = "R"); the two agree to numerical
tolerance.mcmc.options values number_of_iterations,
burn_in and step may now be set to
"auto" (the default), which chooses the MCMC length from
the rarity of the data. Any value given as a number is used exactly as
before.selectModel(): automatic APC model selection by
DIC.prior_scale argument for
method = "pg".checkConvergence() now assesses the identified
quantities (smoothing precisions, intercept and the fitted linear
predictor per Lexis cell), which are invariant to the age-period-cohort
trend aliasing, rather than the raw effect chains that drift along the
non-identified trend.effects.apc() and plot.apc() gain a
convention argument that fixes the non-identified linear
trend to a chosen display gauge, making the effect curves reproducible
between runs; plot.apc() also handles any number of
quantiles and zero-covariate models.print.apc() now also reports the intercept (5%/50%/95%
quantiles).predict_apc(periods = 0) crash (downward-sequence
off-by-one).predict_apc() logit overflow: use
plogis() instead of exp(x)/(1+exp(x)), which
returned NaN for large forecast logits and crashed
downstream.bamp():
age/period/cohort = NULL is now
accepted (previously errored with “argument is of length zero”).predict_apc(): age-period models without a cohort
effect can now be predicted, and non-integer population/exposure no
longer produces NA.bamp(..., method = "pg"): a chain that fails under
forked parallelism (parallel::mclapply) now reports its
actual error instead of the opaque “subscript out of bounds” that
resulted from silently indexing into the failed chain’s result.bamp()’s convergence warning being tied to
verbose backwards: it now prints the “did not converge”
message when verbose = TRUE and stays silent when
verbose = FALSE, as intended.bamp() and stored in the returned object
(model$effects), so a separate call to
effects.apc() is no longer needed for the default median
summary.effects.apc(): cache check was looking for
x$effect (singular) instead of x$effects
(plural), and referenced an undefined variable in the cache
condition.cohort="rw2+het" was not recognized correctly).period_covariate handling that silently
prevented vector coercion.checkConvergence(): cohort convergence check used
age hyperparameter instead of cohort hyperparameter.checkConvergence() info
output.GetRNGstate/PutRNGstate pairing in
random number generation.mclapply with socket-based
makeCluster/parLapply for reliable parallel
execution on all platforms, including macOS GUI environments (RStudio).
Falls back to sequential if cluster setup fails.bibentry().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.
Health stats visible at Monitor.