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glmtree 0.4.0

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.

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.