Package: noisysbmGGM
Title: Noisy Stochastic Block Model for GGM Inference
Version: 0.1.2.3
Authors@R: c(person(given = "Valentin",
           family = "Kilian",
           role = c("aut", "cre"),
           email = "valentin.kilian@ens-rennes.fr"),
    person(given = "Fanny",
           family = "Villers",
           role = "aut",
           email = "fanny.villers@upmc.fr"))
Author: Valentin Kilian [aut, cre],
  Fanny Villers [aut]
Maintainer: Valentin Kilian <valentin.kilian@ens-rennes.fr>
Description: 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) <arXiv:2402.19021>.
License: GPL-2
Encoding: UTF-8
Imports:
        parallel,ppcor,SILGGM,stats,igraph,huge,Rcpp,RcppArmadillo,MASS,RColorBrewer
RoxygenNote: 7.2.3
Depends: R (>= 3.1.0)
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-03-06 14:56:30 UTC; vkilian
Repository: CRAN
Date/Publication: 2024-03-07 10:40:02 UTC
Built: R 4.5.2; x86_64-w64-mingw32; 2025-11-08 03:51:25 UTC; windows
Archs: x64
