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MultiRFM: High-Dimensional Multi-Study Robust Factor Model

We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) <doi:10.48550/arXiv.2506.18478>.

Version: 1.1.0
Depends: R (≥ 3.5.0)
Imports: MASS, irlba, LaplacesDemon, mixtools, mvtnorm, Rcpp (≥ 1.0.8.3)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2025-12-03
DOI: 10.32614/CRAN.package.MultiRFM (may not be active yet)
Author: Wei Liu [aut, cre], Xiaolu Jiang [aut]
Maintainer: Wei Liu <liuweideng at gmail.com>
BugReports: https://github.com/feiyoung/MultiRFM/issues
License: GPL-3
URL: https://github.com/feiyoung/MultiRFM
NeedsCompilation: yes
Materials: README
CRAN checks: MultiRFM results

Documentation:

Reference manual: MultiRFM.html , MultiRFM.pdf
Vignettes: High Dimensional Example of MultiRFM (source, R code)
Low Dimensional Example of MultiRFM (source, R code)

Downloads:

Package source: MultiRFM_1.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): MultiRFM_1.1.0.tgz, r-oldrel (arm64): MultiRFM_1.1.0.tgz, r-release (x86_64): MultiRFM_1.1.0.tgz, r-oldrel (x86_64): MultiRFM_1.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=MultiRFM to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.