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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 9 months agofrom:eb506d204d. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 31 2024 |
Exports:em.covem.precEMggmrhogridstartvals.cov
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glassoglassoFastglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalavaanlifecyclelistenvlubridatemagrittrMASSMatrixmatrixcalcmgcvmnormtModelMetricsmunsellnlmennetnumDerivparallellypbivnormpillarpkgconfigplyrpROCprodlimprogressrproxypurrrquadprogR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
