fdm2id 1.0.0

The package was reviewed with Claude Code. Some forty bugs were fixed as a result, and a test suite was added.

Breaking changes

Results that change: evaluation.precision(), evaluation.recall(), evaluation.kappa(), evaluation.adjr2(), compare.jaccard(), intern.dunn(), CDA(), MLPREG(), LINREG(), TSNE(), correlated(), predict() on SVM, performance (type = "roc" / "cost"), feature selection (CFS, Relief, mRMR), and every split or cross-validation, now stratified (stratify). performance() also re-seeds before each method it is given, so a method that searches a grid no longer scores differently depending on its position in the list.

Arguments:

• the final arguments of every learning method are now nfolds, tune, methodparameters, graph, seed, in that order
• HCA(): second argument
• DBSCAN (epsilonDist) is now eps; EM (clusters) is now k; params is now methodparameters in SVR(), SVRl(), SVRr() and MLPREG()
• new defaults: graph = FALSE everywhere, STUMP (randomvar), FEATURESELECTION (multieval), LINREG (regeval, nrep, validation), CART (xval), loadtext (dir), performance (fuzzy), CA (ncp), MCA (ncp)

Return values: compare (comp = "cluster"), LINREG (reg = "subset"), MLPREG (tune = TRUE), FEATURESELECTION (tune = TRUE).

Errors raised where a value was returned: misspelt comp, type, quali and reg, CDA(), LR(), intern.dunn(), performance().

Removed: HDBSCAN(), ISOFOREST(), UMAP(), the setClass() declarations.

Datasets: ozone has 13 variables instead of 10; movies is now simulated and rated from 1 to 10.

New features

• new methods: GBREG(), PAM(), penalized logistic regression (LR (reg = ))
• predict() on factorial analyses and on every clustering method
• multiclass precision, recall and F-measure (average)
• performance() on a test set given directly, without resampling
• number of clusters: silhouette, gap statistic, elbow
• selectfeatures (unieval = "randomforest") and plot() on a selection
• plotdata (type = "correlation") and plotdata (target = )
• HCA (engine = ), automatic eps in DBSCAN() and number of clusters in HCA()
• seed in every learning method, k in every clustering method
• package objects print a summary
• legendpos in plot() on a CDA object
• faster hyperparameter search, SPECTRAL(), HCA() and feature selection
• eight vignettes, one per case study of the courses, listed by vignette (package = "fdm2id"); they print the same figures as the course handouts, and flag in comments every call whose result moves from one run to the next without a seed