Gaussian Graphical Models with Latent Clustering Structure R package normalblockr version 0.3.0 Implements the Normal-Block model, a Gaussian graphical model with a latent clustering structure for the multivariate analysis of continuous data. The model clusters variables and, building on the graphical lasso, infers a network of statistical dependencies between clusters rather than between individual variables, for known or unknown clusterings, with an optional zero-inflation extension for data with an excess of exact zeros. A complementary family clusters variables by their regression response to covariates rather than by their covariance, sharing one profile per cluster. See Tous & Chiquet (2026) <doi:10.1016/j.csda.2026.108347> for the model itself and its variational expectation-maximization estimation procedure. Software Depends: R (>= 4.1.0) Imports: Matrix, dplyr, tidyr, tibble, purrr, R6, ggplot2, igraph, sbm, corrplot, scales, MASS, stats, Rcpp LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), aricode, covr, Metrics, rmarkdown, pheatmap, paletteer, ggthemes, knitr Julien Chiquet <julien.chiquet@inrae.fr> Comprehensive R Archive Network (CRAN) Jeanne Tous [aut], Nestor Ngalala Manguitini [ctb], Julien Chiquet [aut, cre] (ORCID: <https://orcid.org/0000-0002-3629-3429>) GPL (>= 3) 2026-09-11 application/tgz https://CRAN.R-project.org/package=normalblockr doi:10.32614/CRAN.package.normalblockr