Package: rrMixture 0.1-2

Suyeon Kang

rrMixture: Reduced-Rank Mixture Models

We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

Authors:Suyeon Kang [aut, cre], Weixin Yao [aut], Kun Chen [aut]

rrMixture_0.1-2.tar.gz
rrMixture_0.1-2.tar.gz(r-4.7-arm64)rrMixture_0.1-2.tar.gz(r-4.7-x86_64)rrMixture_0.1-2.tar.gz(r-4.6-arm64)rrMixture_0.1-2.tar.gz(r-4.6-x86_64)
rrMixture_0.1-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
rrMixture/json (API)

# Install 'rrMixture' in R:
install.packages('rrMixture', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

openblascpp

2.00 score 6 scripts 227 downloads 4 exports 7 dependencies

Last updated from:6c87645d9b. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK143
linux-devel-x86_64OK129
source / vignettesOK554
linux-release-arm64OK124
linux-release-x86_64OK130
wasm-releaseOK119

Exports:initialize.pararrmixrrmix.sim.normtune.rrmix

Dependencies:gtoolslatticeMASSMatrixmatrixcalcRcppRcppArmadillo

Introduction to rrMixture

Rendered fromrrMixture.Rmdusingknitr::rmarkdownon Jun 18 2026.

Last update: 2022-04-08
Started: 2022-03-10