Package: EMgaussian 0.2.1

Carl F. Falk

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.

Authors:Carl F. Falk [cre, aut]

EMgaussian_0.2.1.tar.gz
EMgaussian_0.2.1.tar.gz(r-4.5-noble)EMgaussian_0.2.1.tar.gz(r-4.4-noble)
EMgaussian_0.2.1.tgz(r-4.4-emscripten)EMgaussian_0.2.1.tgz(r-4.3-emscripten)
EMgaussian.pdf |EMgaussian.html✨
EMgaussian/json (API)

# Install 'EMgaussian' in R:
install.packages('EMgaussian', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 583 downloads 5 exports 83 dependencies

Last updated 8 months agofrom:eb506d204d. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 01 2024
R-4.5-linux-x86_64NOTEOct 01 2024

Exports:em.covem.precEMggmrhogridstartvals.cov

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glassoglassoFastglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalavaanlifecyclelistenvlubridatemagrittrMASSMatrixmatrixcalcmgcvmnormtModelMetricsmunsellnlmennetnumDerivparallellypbivnormpillarpkgconfigplyrpROCprodlimprogressrproxypurrrquadprogR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr