Package: EMgaussian 0.2.1

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
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EMgaussian_0.2.1.tgz(r-4.4-x86_64)EMgaussian_0.2.1.tgz(r-4.4-arm64)EMgaussian_0.2.1.tgz(r-4.3-x86_64)EMgaussian_0.2.1.tgz(r-4.3-arm64)
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EMgaussian.pdf |EMgaussian.html✨
EMgaussian/json (API)

Peer review:

Bug tracker:https://github.com/falkcarl/emgaussian/issues

On CRAN:

5 exports 1.15 score 83 dependencies 829 downloads

Last updated 6 months agofrom:4bc02c4e9e. Checks:OK: 7 NOTE: 2. Indexed: yes.

Exports:em.covem.precEMggmrhogridstartvals.cov

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glassoglassoFastglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalavaanlifecyclelistenvlubridatemagrittrMASSMatrixmatrixcalcmgcvmnormtModelMetricsmunsellnlmennetnumDerivparallellypbivnormpillarpkgconfigplyrpROCprodlimprogressrproxypurrrquadprogR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr