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) . As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available.


Reference manual

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install.packages("EMgaussian")

0.2.2 by Carl F. Falk, a year ago


Browse source code at https://github.com/cran/EMgaussian


Authors: Carl F. Falk [cre, aut]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports Rcpp, matrixcalc, Matrix, lavaan, glasso, glassoFast, caret

Suggests testthat, psych, bootnet, qgraph, cglasso

Linking to Rcpp, RcppArmadillo


See at CRAN