deepImp 1.1.0

Major rewrite of the package for a configurable, dual-backend neural-network imputer.

New features

• Architecture is now user-configurable via deepimp_arch() (number and size of hidden layers, dropout, activation, batch normalisation, optimiser, learning rate), replacing the previously hard-coded network.
• Two backends: a native 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".
• Results are returned as a "deepimp" object; read the completed data with getImputed() (or as.data.frame()), and inspect with print(), summary(), and plot().
• Reproducible runs via a 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).
• A vignette, vignette("deepImp"), and a package overview page (?deepImp).

Bug fixes

• 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.

Deprecations and breaking changes

• 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.

Notes

• With m > 1, impNNet() / impNNetCoDa() return several completed data sets. These are stochastic replicate completions, not statistically valid multiple imputation.