Conditional Graphical LASSO for Gaussian Graphical Models with Censored and Missing Values
Akaike Information Criterion
Bayesian Information Criterion
Post-Hoc Maximum Likelihood Refitting of a Conditional Graphical Lasso
Internal Functions
Conditional Graphical LASSO for Gaussian Graphical Models with Censore...
Conditional Graphical Lasso Estimator
Extract Model Coefficients
Calculate Column Means and Vars of a datacggm Object
Create a Dataset from a Conditional Gaussian Graphical Model with Cens...
Dimensions of a datacggm Object
Dimnames of a datacggm Object
Status Indicator Matrix from a ‘datacggm’ Object
Extract Model Fitted Values
Retrieve Graphs from a ‘cglasso2igraph’ Object
Retrieve Matrices ‘Y’ and ‘X’ from a ‘datacggm’ Object
Histogram for a datacggm Object
Imputation of Missing and Censored Values
Is an Object of Class cglasso2igraph ?
Is an Object of Class datacggm ?
Lower and Upper Limits from a datacggm Object
Extract the Number of Observations/Responses/Predictors from a datacgg...
Plot Method for a ‘cggm’ Object
Plot Method for ‘cglasso’ Object
Plot Method for a cglasso2igraph Object"
Plot for ‘GoF’ Object
Predict Method for cglasso and cggm Fits
Extract Q-Function
Quantile-Quantile Plots for a datacggm Object
Simulate Data from a Conditional Gaussian Graphical Model with Censore...
Extract Model Residuals
Row and Column Names of a datacggm Object
Model Selection for the Conditional Graphical Lasso Estimator
Show Package Structure
Summarizing cglasso and cggm Fits
Summarizing Objects of Class ‘datacggm’
Create Graphs from cglasso or cggm Objects
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>.