swMATH ID: 44217
Software Authors: Luigi Augugliaro, Gianluca Sottile, Ernst C. Wit, Veronica Vinciotti
Description: R package cglasso: Conditional Graphical LASSO for Gaussian Graphical Models with Censored and Missing Values. Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) <doi:10.18637/jss.v105.i01>, Augugliaro et al. (2020b) <doi:10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi:10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi:10.1214/11-AOAS494> and Stadler et al. (2012) <doi:10.1007/s11222-010-9219-7>.
Homepage: https://cran.r-project.org/web/packages/cglasso/index.html
Source Code:  https://github.com/cran/cglasso
Dependencies: R
Keywords: cglasso; conditional Gaussian graphical models; glasso; high-dimensionality; sparsity; censoring; missing data; JSS Journal of Statistical Software
Related Software: BayesianGLasso; BigQuic; glassoFast; MASS (R); igraph; Monomvn; sparsebn; BGGM; bnstruct; bnlearn; BDgraph; huge; QUIC; glasso; R
Cited in: 0 Publications

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