Package: mpem 0.1.0

mpem: Matrix Partial EM for Incomplete Matrix-Normal Data

Fits single-component and finite-mixture Kronecker-structured matrix-normal models and imputes incomplete matrix-variate data using matrix partial expectation-maximization. General MPEM handles arbitrary missingness, while Rect-MPEM exploits rectangular structural missingness. The methods are described in Lu, Andrews and Browne (2026) "An Efficient EM Algorithm for Both Element-Wise and Structural Missingness in Matrix-Variate Normal Mixture Models" <doi:10.48550/arXiv.2609.00616>.

Authors:Hanzhang Lu [aut, cre, cph], Jeffrey L. Andrews [aut, ths], Ryan P. Browne [aut]

mpem_0.1.0.tar.gz
mpem_0.1.0.zip(r-4.7-x86_64)mpem_0.1.0.zip(r-4.7-arm64)mpem_0.1.0.zip(r-4.6-x86_64)mpem_0.1.0.zip(r-4.6-arm64)mpem_0.1.0.zip(r-4.5-x86_64)
mpem_0.1.0.tgz(r-4.6-x86_64)mpem_0.1.0.tgz(r-4.6-arm64)mpem_0.1.0.tgz(r-4.5-x86_64)mpem_0.1.0.tgz(r-4.5-arm64)
mpem_0.1.0.tar.gz(r-4.7-arm64)mpem_0.1.0.tar.gz(r-4.7-x86_64)mpem_0.1.0.tar.gz(r-4.6-arm64)mpem_0.1.0.tar.gz(r-4.6-x86_64)
mpem_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mpem/json (API)

# Install 'mpem' in R:

install.packages('mpem', repos = c('https://lhzmix.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lhzmix/mpem/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

openblas cpp

2.70 score 1 exports 2 dependencies

Last updated from:e20374dcf5. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	118
linux-devel-x86_64	OK	131
source / vignettes	OK	171
linux-release-arm64	OK	120
linux-release-x86_64	OK	170
macos-release-arm64	OK	137
macos-release-x86_64	OK	298
macos-oldrel-arm64	OK	136
macos-oldrel-x86_64	OK	382
windows-devel-arm64	OK	357
windows-devel-x86_64	OK	130
windows-release-arm64	OK	397
windows-release-x86_64	OK	99
windows-oldrel-x86_64	OK	98
wasm-release	OK	106