Description Details Author(s) References

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.

Package: | cglasso |

Type: | Package |

Version: | 2.0.4 |

Date: | 2021-04-09 |

License: | GPL (>=2) |

Luigi Augugliaro [aut, cre],

Gianluca Sottile [aut]

Ernst C. Wit [aut]

Veronica Vinciotti [aut]

Maintainer: Luigi Augugliaro <luigi.augugliaro@unipa.it>

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>.
*l1*-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.

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