Changelog • normalblockr

normalblockr 0.2.1

First CRAN submission

S3 methods print(), summary(), plot(), logLik() and BIC() for fitted models (any NormalBlockVarBase subclass), and print()/summary()/logLik()/BIC() for collections of models; accessing $loglik on a collection now raises an informative error instead of silently returning NULL.
Addressed reviewer feedback ahead of CRAN submission: shortened/title-cased man page titles, added missing @examples, cross-referenced $plot_network()/$latent_network() in each other’s documentation, and replaced a few inefficient matrix operations (solve() on symmetric positive-definite matrices, M %*% t(C)) with chol2inv(chol())/tcrossprod().

Zero-inflation extension (ZINormalBlockVarKnownClusters/ZINormalBlockVarUnknownClusters) for data with an excess of exact zeros.
Sparsity path on the cluster-level precision matrix (graphical lasso), with warm-starting across penalties (NormalBlockVarCollectionSparsity, sparsity = TRUE in normal_block()).
Accelerated variational EM (SQUAREM-style extrapolation) for both known- and unknown-clustering models.
Several clustering-initialization heuristics (ward2, kmeans, spectral, sbm, selectable via NB_control(clustering_init = )), and best_of_inits() to try several and keep the best-ELBO fit.
refine() on NormalBlockVarCollectionClusters: post-hoc split/merge search seeded from neighboring cluster counts, to escape mediocre local optima left by independent per-q cold starts.
New real datasets: brca_rppa (breast cancer proteomics), onema (French stream fish biomass, zero-inflated), university (WebKB text data), each with a dedicated vignette.

Renamed the model classes (NormalBlock* to NormalBlockVar*) for consistency; NormalBlockData is unchanged.
NormalBlockData rescales columns of Y by default; fitted values and regression coefficients are reported back on the original scale.
Removed the ClustOfVar dependency (the kmeansvar clustering heuristic was dropped after benchmarking showed it was both the worst-ranked and least reliable of the available heuristics).
Package cleanup for CRAN submission: license, documentation, and package structure.

Initial implementation of the Normal-Block model: a Gaussian graphical model with a latent clustering structure, for known or unknown clusterings, fit by variational EM.