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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 |
| 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 |
| Reference manual: | noisysbmGGM.pdf |
| Vignettes: |
User guide for the noisysbmGGM package |
| Package source: | noisysbmGGM_0.1.2.3.tar.gz |
| Windows binaries: | r-devel: noisysbmGGM_0.1.2.3.zip, r-release: noisysbmGGM_0.1.2.3.zip, r-oldrel: noisysbmGGM_0.1.2.3.zip |
| 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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