Load GFM package | Load rrpack and PCAmixdata packages for other methods | Introduction to the data generation mechanisms | Brief description of other methods | OverGFM can handle overdispersed mixed-type data | Other methods poorly handle overdispersed mixed-type data | Visualization | Session information
This package is considered a duplicate. The official version of this package is found at:https://feiyoung.r-universe.dev/GFM
Package: GFM 1.2.2
Wei Liu
GFM: Generalized Factor Model
Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. This model can be employed in social and behavioral sciences, economy and finance, and genomics, to extract interpretable nonlinear factors. More details can be referred to Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2023) <doi:10.1080/01621459.2021.1999818>.
Authors:
GFM_1.2.2.tar.gz
GFM_1.2.2.zip(r-4.7-x86_64)GFM_1.2.2.zip(r-4.7-arm64)GFM_1.2.2.zip(r-4.6-x86_64)GFM_1.2.2.zip(r-4.6-arm64)
GFM_1.2.2.tar.gz(r-4.7-arm64)GFM_1.2.2.tar.gz(r-4.7-x86_64)GFM_1.2.2.tar.gz(r-4.6-arm64)GFM_1.2.2.tar.gz(r-4.6-x86_64)
GFM_1.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GFM/json (API)
| # Install 'GFM' in R: |
| install.packages('GFM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/feiyoung/gfm/issues
Last updated from:429b3b8774. Checks:8 WARNING, 2 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 112 | ||
| linux-devel-x86_64 | WARNING | 378 | ||
| source / vignettes | OK | 220 | ||
| linux-release-arm64 | WARNING | 158 | ||
| linux-release-x86_64 | WARNING | 158 | ||
| windows-devel-arm64 | WARNING | 260 | ||
| windows-devel-x86_64 | WARNING | 166 | ||
| windows-release-arm64 | WARNING | 227 | ||
| windows-release-x86_64 | WARNING | 295 | ||
| wasm-release | OK | 103 |
Exports:chooseFacNumberFactormgendatagfmmeasurefunoverdispersedGFMOverGFMchooseFacNumber
Dependencies:codetoolsdoSNOWforeachirlbaiteratorslatticeMASSMatrixRcppRcppArmadillosnow
OverGFM: simulated examples
Last update: 2026-01-18
Started: 2026-01-18
GFM: A Simple Transcriptomics Data
Load real data | Fit GFM model | Compare with LFM in downstream analysis
Last update: 2023-08-11
Started: 2022-01-05
GFM: alternate maximization and information criterion
Fit GFM model using simulated data | GFM can handle data with homogeneous normal variables | GFM outperforms LFM in analyzing data with heterogeous normal variables | GFM outperforms LFM in analyzing data with Count(Poisson) variables | GFM outperforms LFM in analyzing data with the mixed-types of count and categorical variables | Session information
Last update: 2023-08-11
Started: 2022-01-05
Installation
Install the GFM
Last update: 2023-08-11
Started: 2022-01-05
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Choose the Number of factors for Generalized Factor Models | chooseFacNumber |
| Factor Analysis Model | Factorm |
| Generate simulated data | gendata |
| Generalized Factor Model | gfm |
| Assess the performance of an estimator on a matrix | measurefun |
| Overdispersed Generalized Factor Model | overdispersedGFM |
| Choose the Number of factors for Overdispersed Generalized Factor Models | OverGFMchooseFacNumber |