Greedy Bayesian algorithm to fit the noisy stochastic block model to an observed sparse graph. Moreover, a graph inference procedure to recover Gaussian Graphical Model (GGM) from real data. This procedure comes with a control of the false discovery rate. The method is described in the article "Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure" by Kilian, Rebafka, and Villers (2024) <doi:10.48550/arXiv.2402.19021>.
Version: 0.1.2.3 Depends: R (≥ 3.1.0) Imports: parallel, ppcor, SILGGM, stats, igraph, huge, Rcpp, RcppArmadillo, MASS, RColorBrewer LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown Published: 2024-03-07 DOI: 10.32614/CRAN.package.noisysbmGGM Author: Valentin Kilian [aut, cre], Fanny Villers [aut] Maintainer: Valentin Kilian <valentin.kilian at ens-rennes.fr> License: GPL-2 NeedsCompilation: yes CRAN checks: noisysbmGGM results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=noisysbmGGM to link to this page.
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