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>.
