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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.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.