The \(\ell_1\)-penalized censored Gaussian graphical model (Augugliaro and other, 2018) is an extension of the graphical lasso estimator (Yuan and other, 2007) developed to handle datasets from a censored Gaussian graphical model. An EM-like algorithm is implemented to fit the model. The graphical lasso algorithm (Friedman and other, 2008) is used to solve the maximization problem in the M-step.
| Package: | cglasso |
| Type: | Package |
| Version: | 1.1.2 |
| Date: | 2020-07-20 |
Augugliaro, L., Abbruzzo, A., and Vinciotti, V. (2018) <DOI:10.1093/biostatistics/kxy043>. \(\ell_1\)-Penalized gaussian graphical model. Biostatistics (to appear).
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.
Yuan, M., and Lin, Y. (2007) <DOI:10.1093/biomet/asm018>. Model selection and estimation in the Gaussian graphical model. Biometrika 94, 19--35.