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.
Luigi Augugliaro [aut, cre],
Gianluca Sottile [aut]
Ernst C. Wit [aut]
Veronica Vinciotti [aut]
Maintainer: Luigi Augugliaro <luigi.augugliaro@unipa.it>
| Package: | cglasso |
| Type: | Package |
| Version: | 2.0.6 |
| Date: | 2023-01-17 |
| License: | GPL (>=2) |
Augugliaro L., Sottile G., Wit E.C., and Vinciotti V. (2023) <tools:::Rd_expr_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) <tools:::Rd_expr_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) <tools:::Rd_expr_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) <tools:::Rd_expr_doi("10.1093/biostatistics/kxm045")>. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432--441.
Yin, J. and Li, H. (2001) <tools:::Rd_expr_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) <tools:::Rd_expr_doi("10.1093/biomet/asm018")>. Model selection and estimation in the Gaussian graphical model. Biometrika 94, 19--35.