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
DOI: 10.32614/CRAN.package.EMgaussian
Author: Carl F. Falk [cre, aut]
Maintainer: Carl F. Falk <carl.falk at mcgill.ca>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: EMgaussian results


Reference manual: EMgaussian.pdf


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


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