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EMgaussian  

Expectation-Maximization Algorithm for Multivariate Normal (Gaussian) with Missing Data
View on CRAN: Click here


Download and install EMgaussian package within the R console
Install from CRAN:
install.packages("EMgaussian")

Install from Github:
library("remotes")
install_github("cran/EMgaussian")

Install by package version:
library("remotes")
install_version("EMgaussian", "0.2.1")



Attach the package and use:
library("EMgaussian")
Maintained by
Carl F. Falk
[Scholar Profile | Author Map]
First Published: 2024-03-04
Latest Update: 2024-03-04
Description:
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 St
How to cite:
Carl F. Falk (2024). EMgaussian: Expectation-Maximization Algorithm for Multivariate Normal (Gaussian) with Missing Data. R package version 0.2.1, https://cran.r-project.org/web/packages/EMgaussian. Accessed 02 Apr. 2025.
Previous versions and publish date:
No previous versions
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Complete documentation for EMgaussian
Functions, R codes and Examples using the EMgaussian R package
Full EMgaussian package functions and examples
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