EMgaussian: Expectation-Maximization Algorithm for Multivariate Normal (Gaussian) with Missing Data

Initially designed to distribute code for estimating the Gaussian graphical model with Lasso regularization, also known as the graphical lasso (glasso), using an Expectation-Maximization (EM) algorithm based on work by Städler and Bühlmann (2012) <doi:10.1007/s11222-010-9219-7>. As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available.

Version: 0.2.1
Imports: Rcpp, matrixcalc, Matrix, lavaan, glasso, glassoFast, caret
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0), psych, bootnet, qgraph, cglasso
Published: 2024-03-04
Author: Carl F. Falk [cre, aut]
Maintainer: Carl F. Falk <carl.falk at mcgill.ca>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: EMgaussian results

Documentation:

Reference manual: EMgaussian.pdf

Downloads:

Package source: EMgaussian_0.2.1.tar.gz
Windows binaries: r-prerel: EMgaussian_0.2.1.zip, r-release: EMgaussian_0.2.1.zip, r-oldrel: EMgaussian_0.2.1.zip
macOS binaries: r-prerel (arm64): EMgaussian_0.2.1.tgz, r-release (arm64): EMgaussian_0.2.1.tgz, r-oldrel (arm64): EMgaussian_0.2.1.tgz, r-prerel (x86_64): EMgaussian_0.2.1.tgz, r-release (x86_64): EMgaussian_0.2.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=EMgaussian to link to this page.