The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

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.