Package: rrMixture 0.1-2

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]

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rrMixture.pdf |rrMixture.html✨
rrMixture/json (API)

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

Peer review:

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

On CRAN:

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

4 exports 0.00 score 7 dependencies 5 scripts 287 downloads

Last updated 2 years agofrom:6c87645d9b. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 23 2024
R-4.5-win-x86_64OKAug 23 2024
R-4.5-linux-x86_64OKAug 23 2024
R-4.4-win-x86_64OKAug 23 2024
R-4.4-mac-x86_64OKAug 23 2024
R-4.4-mac-aarch64OKAug 23 2024
R-4.3-win-x86_64OKAug 23 2024
R-4.3-mac-x86_64OKAug 23 2024
R-4.3-mac-aarch64OKAug 23 2024

Exports:initialize.pararrmixrrmix.sim.normtune.rrmix

Dependencies:gtoolslatticeMASSMatrixmatrixcalcRcppRcppArmadillo

Introduction to rrMixture

Rendered fromrrMixture.Rmdusingknitr::rmarkdownon Aug 23 2024.

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