cglasso-package {cglasso}

Conditional Graphical LASSO for Gaussian Graphical Models with Censored and Missing Values

Description

Conditional graphical lasso (cglasso) estimator (Yin and other, 2011) is an extension of the graphical lasso (Yuan and other, 2007) 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 (Augugliaro and other, 2020a and Augugliaro and other, 2020b). Standard conditional graphical lasso is available as a special case. Furthermore, the cglasso 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

Package:	cglasso
Type:	Package
Version:	2.0.6
Date:	2023-01-17
License:	GPL (>=2)

Author(s)

Luigi Augugliaro [aut, cre],
Gianluca Sottile [aut]
Ernst C. Wit [aut]
Veronica Vinciotti [aut]
Maintainer: Luigi Augugliaro <[e-mail]>

References

Augugliaro L., Sottile G., Wit E.C., and Vinciotti V. (2023) <doi:10.18637/jss.v105.i01>. cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values. Journal of Statistical Software 105(1), 1–58.

Augugliaro, L., Sottile, G., and Vinciotti, V. (2020a) <doi:10.1007/s11222-020-09945-7>. The conditional censored graphical lasso estimator. Statistics and Computing 30, 1273–1289.

Augugliaro, L., Abbruzzo, A., and Vinciotti, V. (2020b) <doi:10.1093/biostatistics/kxy043>. \ell_1-Penalized censored Gaussian graphical model. Biostatistics 21, e1–e16.

Friedman, J.H., Hastie, T., and Tibshirani, R. (2008) <doi:10.1093/biostatistics/kxm045>. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432–441.

Yin, J. and Li, H. (2001) <doi:10.1214/11-AOAS494>. A sparse conditional Gaussian graphical model for analysis of genetical genomics data. The Annals of Applied Statistics 5(4), 2630–2650.

Yuan, M., and Lin, Y. (2007) <doi:10.1093/biomet/asm018>. Model selection and estimation in the Gaussian graphical model. Biometrika 94, 19–35.