Package: CMGFM 1.1

Wei Liu

CMGFM: 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.tar.gz(r-4.5-noble)CMGFM_1.1.tar.gz(r-4.4-noble)
CMGFM_1.1.tgz(r-4.4-emscripten)CMGFM_1.1.tgz(r-4.3-emscripten)
CMGFM.pdf |CMGFM.html✨
CMGFM/json (API)

# Install 'CMGFM' in R:
install.packages('CMGFM', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/feiyoung/cmgfm/issues0 issues

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

On CRAN:

Conda:

openblascppopenmp

2.70 score 535 downloads 3 exports 12 dependencies

Last updated 9 months agofrom:92b717acc0. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 23 2025
R-4.5-linux-x86_64OKMar 23 2025
R-4.4-linux-x86_64OKMar 23 2025

Exports:CMGFMgendata_cmgfmMSVR

Dependencies:codetoolsdoSNOWforeachGFMirlbaiteratorslatticeMASSMatrixRcppRcppArmadillosnow

CMGFM: simulation

Wei Liu

Rendered fromsimu.Rmdusingknitr::rmarkdownon Mar 23 2025.

Last update: 2024-06-26
Started: 2024-06-26

Citation

To cite package ‘CMGFM’ in publications use:

Liu W, Jiang J, Xiang D, Zhou X (2024). CMGFM: Covariate-Augumented Generalized Factor Model. R package version 1.1, https://CRAN.R-project.org/package=CMGFM.

Corresponding BibTeX entry: 

  @Manual{,
    title = {CMGFM: Covariate-Augumented Generalized Factor Model},
    author = {Wei Liu and Jiakun Jiang and Dewei Xiang and Xuancheng
      Zhou},
    year = {2024},
    note = {R package version 1.1},
    url = {https://CRAN.R-project.org/package=CMGFM},
  }

Readme and manuals

CMGFM

High-Dimensional Covariate-Augmented Generalized Factor Model

=========================================================================

Existing methods for multi-omics representation learning often lack interpretability or overlook critical omics-specific and additional information. To address these limitations and meet the practical demands, we introduce CMGFM, an interpretable multi-omics representation learning approach via covariate-augumented generalized factor model. CMGFM 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.

Check out our Package Website for a more complete description of the methods and analyses.

Installation

"CMGFM" depends on the 'Rcpp' and 'RcppArmadillo' package, which requires appropriate setup of computer. For the users that have set up system properly for compiling C++ files, the following installation command will work.

## Method 1：
if (!require("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_github("feiyoung/CMGFM")

## Method 2: install from CRAN
install.packages("CMGFM")

Usage

For usage examples and guided walkthroughs, check the vignettes directory of the repo.

Simulated codes

For the codes in simulation study, check the simu_code directory of the repo.

News

CMGFM version 1.1 released! (2024-06-23)