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. (2021) <doi:10.1080/01621459.2021.1999818>.
Version: | 1.2.1 |
Depends: | doSNOW, parallel, R (≥ 3.5.0) |
Imports: | MASS, stats, irlba, Rcpp, methods |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | knitr, rmarkdown |
Published: | 2023-08-11 |
DOI: | 10.32614/CRAN.package.GFM |
Author: | Wei Liu [aut, cre], Huazhen Lin [aut], Shurong Zheng [aut], Jin Liu [aut], Jinyu Nie [aut] |
Maintainer: | Wei Liu <LiuWeideng at gmail.com> |
BugReports: | https://github.com/feiyoung/GFM/issues |
License: | GPL-3 |
URL: | https://github.com/feiyoung/GFM |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | GFM results |
Reference manual: | GFM.pdf |
Vignettes: |
GFM: A Simple Transcriptomics Data GFM: alternate maximization and information criterion Installation |
Package source: | GFM_1.2.1.tar.gz |
Windows binaries: | r-devel: GFM_1.2.1.zip, r-release: GFM_1.2.1.zip, r-oldrel: GFM_1.2.1.zip |
macOS binaries: | r-release (arm64): GFM_1.2.1.tgz, r-oldrel (arm64): GFM_1.2.1.tgz, r-release (x86_64): GFM_1.2.1.tgz, r-oldrel (x86_64): GFM_1.2.1.tgz |
Old sources: | GFM archive |
Reverse imports: | CMGFM, MMGFM |
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