iClusterVB: Fast Integrative Clustering and Feature Selection for High Dimensional Data

A variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.

Version: 0.1.4
Depends: R (≥ 4.0.0)
Imports: cluster, clustMixType, cowplot, ggplot2, graphics, grDevices, mclust, MCMCpack, mvtnorm, pheatmap, poLCA, Rcpp (≥ 1.0.12), stats, utils, VarSelLCM
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, survival, survminer
Published: 2024-12-09
DOI: 10.32614/CRAN.package.iClusterVB
Author: Abdalkarim Alnajjar ORCID iD [aut, cre, cph], Zihang Lu [aut]
Maintainer: Abdalkarim Alnajjar <abdalkarim.alnajjar at queensu.ca>
BugReports: https://github.com/AbdalkarimA/iClusterVB/issues
License: MIT + file LICENSE
URL: https://github.com/AbdalkarimA/iClusterVB
NeedsCompilation: yes
Materials: README
CRAN checks: iClusterVB results

Documentation:

Reference manual: iClusterVB.pdf
Vignettes: Introduction to iClusterVB (source, R code)

Downloads:

Package source: iClusterVB_0.1.4.tar.gz
Windows binaries: r-devel: not available, r-release: iClusterVB_0.1.4.zip, r-oldrel: not available
macOS binaries: r-release (arm64): iClusterVB_0.1.4.tgz, r-oldrel (arm64): iClusterVB_0.1.4.tgz, r-release (x86_64): iClusterVB_0.1.4.tgz, r-oldrel (x86_64): iClusterVB_0.1.4.tgz
Old sources: iClusterVB archive

Linking:

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