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-4.5-noble)rrMixture_0.1-2.tar.gz(r-4.4-noble)
rrMixture_0.1-2.tgz(r-4.4-emscripten)rrMixture_0.1-2.tgz(r-4.3-emscripten)
rrMixture.pdf |rrMixture.html✨
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

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

2.00 score 5 scripts 208 downloads 4 exports 7 dependencies

Last updated 3 years agofrom:6c87645d9b. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Oct 08 2024
R-4.5-linux-x86_64	OK	Oct 08 2024

Exports:initialize.para rrmix rrmix.sim.norm tune.rrmix

Dependencies:gtools lattice MASS Matrix matrixcalc Rcpp RcppArmadillo

Introduction to rrMixture

Rendered fromrrMixture.Rmdusingknitr::rmarkdownon Oct 08 2024.

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

Citation

Readme and manuals

Help Manual

Help page	Topics
Initialization of Parameter Estimates	initialize.para
Visualize rrmix Objects	plot plot.rrmix plot.tune.rrmix
Reduced-Rank Mixture Models in Multivariate Regression	rrmix
Simulation Data Generator	rrmix.sim.norm
rrMixture: Reduced-Rank Mixture Models.	rrMixture
Summarize rrmix Objects	summary summary.rrmix summary.tune.rrmix
Reduced-rank mixture models with optimal tuning parameter(s)	tune.rrmix