cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values
Dublin Core
Title
cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values
Subject
cglasso, conditional Gaussian graphical models, glasso, high-dimensionality, sparsity, censoring, missing data.
Description
Sparse graphical models have revolutionized multivariate inference. With the advent
of high-dimensional multivariate data in many applied fields, these methods are able to
detect a much lower-dimensional structure, often represented via a sparse conditional
independence graph. There have been numerous extensions of such methods in the past
decade. Many practical applications have additional covariates or suffer from missing or
censored data. Despite the development of these extensions of sparse inference methods
for graphical models, there have been so far no implementations for, e.g., conditional
graphical models.
Here we present the general-purpose package cglasso for estimating sparse conditional
Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ1-penalized likelihood via a
block-coordinate descent algorithm. The package has a user-friendly data manipulation
interface. It estimates a solution path and includes various automatic selection algorithms
for the two ℓ1 tuning parameters, associated with the sparse precision matrix and sparse
regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred
conditional independence graphs.
This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of
missing and censored data. As such it constitutes an important contribution for empirical
scientists wishing to detect sparse structures in high-dimensional data.
of high-dimensional multivariate data in many applied fields, these methods are able to
detect a much lower-dimensional structure, often represented via a sparse conditional
independence graph. There have been numerous extensions of such methods in the past
decade. Many practical applications have additional covariates or suffer from missing or
censored data. Despite the development of these extensions of sparse inference methods
for graphical models, there have been so far no implementations for, e.g., conditional
graphical models.
Here we present the general-purpose package cglasso for estimating sparse conditional
Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ1-penalized likelihood via a
block-coordinate descent algorithm. The package has a user-friendly data manipulation
interface. It estimates a solution path and includes various automatic selection algorithms
for the two ℓ1 tuning parameters, associated with the sparse precision matrix and sparse
regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred
conditional independence graphs.
This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of
missing and censored data. As such it constitutes an important contribution for empirical
scientists wishing to detect sparse structures in high-dimensional data.
Creator
Luigi Augugliaro
Source
https://www.jstatsoft.org/article/view/v105i01
Publisher
University of Palermo
Date
January 2023
Contributor
Fajar Bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Luigi Augugliaro, “cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values,” Repository Horizon University Indonesia, accessed April 5, 2025, https://repository.horizon.ac.id/items/show/8282.