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Major rewrite of the package for a configurable, dual-backend neural-network imputer.
deepimp_arch() (number and size of hidden layers, dropout,
activation, batch normalisation, optimiser, learning rate), replacing
the previously hard-coded network.torch backend (default; no
Python required) and an optional keras3 backend, selected
with backend = "torch" / "keras".impNNetCoDa() reimplemented on the shared imputation
engine for rounded zeros in compositional data, with
correction = "truncate" (default, Templ 2021),
"expectation", or "none"."deepimp" object; read the
completed data with getImputed() (or
as.data.frame()), and inspect with print(),
summary(), and plot().seed argument. Reproducibility
is exact when dropout = 0; with dropout > 0
the backend’s training-time randomness is not fully pinned by the seed
in the current torch build (repeated runs are close but may not be
bit-identical).vignette("deepImp"), and a package overview
page (?deepImp).impNNetCoDa(initialize = "kNNa") no longer aborts at
high rounded-zero rates. The compositional-kNN initialiser
(robCompositions::impKNNa()) can fail with
'probs' not in [0, 1] when a large fraction of every part
falls below the detection limit; the initialiser now falls back to
knn (with a warning) instead of stopping the
imputation.impNNet() was redesigned. Legacy arguments
dense, small, normalize_x, and
normalize_y are soft-deprecated (a warning maps them onto
the new API). The arguments model_numeric,
model_binary, model_nominal, and
loss were removed; configure the network through
arch and backend instead.m > 1, impNNet() /
impNNetCoDa() return several completed data sets. These are
stochastic replicate completions, not statistically valid multiple
imputation.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.