pool_coxph(), pool_glm(),
pool_lm(), pool_survreg(), and
pool_clogit(), convenience helpers that pool a list of
fitted models (one per completed data set) using Rubin’s rules and print
a coefficient table formatted like the corresponding base-R
summary() output (summary.coxph(),
summary.glm(), summary.lm(),
summary.survreg(); pool_clogit() matches
pool_coxph() since clogit() fits a stratified
Cox model internally). Degrees of freedom use the Barnard and Rubin
(1999) correction rather than the classic Rubin (1987) formula, which
can diverge to implausibly large values when between-imputation variance
is small relative to within-imputation variance; all five were
cross-validated against mice::pool() to numerical precision
on matched examples.densemlp imputer, wrapping the
densemlp package’s dense multilayer perceptron as a
standard fit/predict learner inside the chained imputation loop (numeric
targets predict a point estimate, categorical targets draw from
predicted class probabilities).missknn imputer, wrapping the
missknn package’s whole-table masked k-nearest-neighbor
engine. Because missknn imputes all variables jointly in
one pass rather than per-variable, selecting it bypasses the
chained-equations loop entirely as a distinct single-shot strategy.progress argument to impute(),
showing an elapsed/ETA progress bar over completed datasets via
functionals::fmap(pb = TRUE). Defaults to TRUE
in interactive sessions when verbose = FALSE, and has no
effect when imputer = "missknn".tibble dependency package-wide. All tabular
results previously returned as tibbles (describe(),
impute(), complete(), evaluate(),
pool(), imputer_registry(), and friends) are
now data.tables instead. Internal row-binding of
trace/diagnostic/pooling data frames also moved from base
do.call(rbind, ...) to
data.table::rbindlist(), the shared backend behind
.rbind_or_empty(). The engine internals (chained-equations
loop, amputation) still operate on plain data.frames to
keep base-R subsetting semantics intact; only the user-facing return
objects changed class.superlearner and sl imputers. These
construct a Super Learner-style ensemble by cross-validating candidate
imputers on observed cells, assigning non-negative loss-based weights,
and combining predictions inside the existing chained-imputation
loop.library, folds, and
metalearner hyperparameters for
superlearner.First public release candidate.
ncore to impute() for
completed-dataset-level parallel imputation through
functionals::fmap().mimar_imputation
diagnostics for convergence screening.mimar
a distinct visual identity while retaining the existing plot
themes.funcml dependency.