Changes in version 0.4.0 (2026-10-11)                  

This release re-submits the package to CRAN, from which it was archived
on 2025-10-03: the vignette called library(FactoMineR) unconditionally
while FactoMineR is only suggested, and the build failed on a check
flavor where it was not installed. The vignette is rewritten and the
optional chunk is only evaluated when FactoMineR is available.

Bug fixes

  - test and validation now do what the documentation says. Until now
    the held out sets were drawn but never used: all regressions, trees
    and criteria were computed on all the observations. The training set
    is now the only data used to fit; validation selects the iteration;
    test is used only to report the performance of the best tree
    (performance$performance).
  - The validation versions of the criteria were broken in
    version 0.3.1: criterion = "aic" with validation = TRUE failed with
    "factor x1 has new levels", the BIC returned -Inf, and the Gini
    mixed the labels of all validation observations with the predictions
    of one segment. All are now computed correctly (log-likelihood or
    Gini on the held out set).
  - The BIC penalty of a segment used log(n) of the whole data set
    because of an operator precedence error; it now uses the size of the
    segment, as intended.
  - With fewer than 50 iterations the "best" tree was always the one of
    iteration 2, drawn from a random segmentation, and iterations = 1
    failed with "object 'best_link' not found". The burn-in is now the
    argument burn_in (default: the minimum of 50 and a quarter of
    iterations) and a tree is always returned.
  - Integer columns, tibbles, matrices, character and logical columns
    are accepted in x; logical and factor responses are accepted for y.
    Missing values are reported with an error.
  - normalizedGini() is computed from ranks (2 AUC - 1): it no longer
    depends on the order of the observations when predictions are tied,
    and returns NA if there is a single class.
  - The class documentation was filed under glmdisc-class; it is now
    glmtree-class.

New features

  - predict() for glmtree objects (probabilities, or type = "segment"),
    robust to factor levels unseen in training (they get the rate of
    positives of their segment); show() / print().
  - New arguments burn_in, patience (stop the chain when the criterion
    stopped improving) and verbose (the criterion was printed at every
    iteration; it is now silent by default).
  - best.tree also stores coefficients, labels, rates etc. used to
    predict; the glm objects of best.tree$glms are named by segment.
  - performance$best_iteration and parameters$sizes (training /
    validation / test sizes).

Changes

  - The SEM loop was rewritten: one glm.fit per segment on a design
    matrix built once instead of glm() + predict() on the whole data
    frame for each segment and iteration, a single prediction of the
    tree per iteration instead of two, vectorized sampling of the
    segments instead of apply(sample()) over the rows. See
    inst/benchmarks/speed.R for the timings.
  - Dependencies reduced to partykit (dplyr, magrittr and caret are no
    longer needed); FactoMineR is only used in an optional vignette
    chunk.
  - Defaults as documented: test = FALSE, validation = FALSE, criterion
    = "bic".
  - generateData() is vectorized (same distributions, different random
    stream).
  - CI: standard r-lib/actions workflows (check on Linux, macOS and
    Windows plus a job without the suggested packages, coverage, styler,
    pkgdown) replace the previous single workflow; renv, Travis and the
    committed docs/ were removed.
  - Added inst/benchmarks/, a comparison with a logistic regression,
    ctree, model-based recursive partitioning and a random forest on
    simulated and real data.