noisysbmGGM: Noisy Stochastic Block Model for GGM Inference

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>.

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>
License: GPL-2
NeedsCompilation: yes
CRAN checks: noisysbmGGM results


Reference manual: noisysbmGGM.pdf
Vignettes: User guide for the noisysbmGGM package


Package source: noisysbmGGM_0.1.2.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): noisysbmGGM_0.1.2.3.tgz, r-oldrel (arm64): noisysbmGGM_0.1.2.3.tgz, r-release (x86_64): noisysbmGGM_0.1.2.3.tgz, r-oldrel (x86_64): noisysbmGGM_0.1.2.3.tgz


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