Package: CMGFM 1.1

Wei Liu

CMGFM: Interpretable Multi-Omics Representation Learning via Covariate-Augumented Generalized Factor Model

Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency.

Authors:Wei Liu [aut, cre], Jiakun Jiang [aut], Dewei Xiang [aut], Xuancheng Zhou [aut]

CMGFM_1.1.tar.gz
CMGFM_1.1.zip(r-4.7)CMGFM_1.1.zip(r-4.6)CMGFM_1.1.zip(r-4.5)
CMGFM_1.1.tgz(r-4.6-x86_64)CMGFM_1.1.tgz(r-4.6-arm64)CMGFM_1.1.tgz(r-4.5-x86_64)CMGFM_1.1.tgz(r-4.5-arm64)
CMGFM_1.1.tar.gz(r-4.7-arm64)CMGFM_1.1.tar.gz(r-4.7-x86_64)CMGFM_1.1.tar.gz(r-4.6-arm64)CMGFM_1.1.tar.gz(r-4.6-x86_64)
CMGFM_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CMGFM/json (API)

# Install 'CMGFM' in R:

install.packages('CMGFM', repos = c('https://feiyoung.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/feiyoung/cmgfm/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

openblas cpp openmp

3.70 score 1 stars 584 downloads 3 exports 12 dependencies

Last updated from:874b4c8adc. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	141
linux-devel-x86_64	NOTE	149
source / vignettes	OK	202
linux-release-arm64	NOTE	138
linux-release-x86_64	NOTE	122
macos-release-arm64	NOTE	96
macos-release-x86_64	NOTE	280
macos-oldrel-arm64	NOTE	136
macos-oldrel-x86_64	NOTE	292
windows-devel	NOTE	143
windows-release	NOTE	127
windows-oldrel	NOTE	117
wasm-release	OK	116