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:Wei Liu [aut, cre], Huazhen Lin [aut], Shurong Zheng [aut], Jin Liu [aut], Jinyu Nie [aut]

GFM_1.2.2.tar.gz
GFM_1.2.2.zip(r-4.7)GFM_1.2.2.zip(r-4.6)GFM_1.2.2.zip(r-4.5)
GFM_1.2.2.tgz(r-4.6-x86_64)GFM_1.2.2.tgz(r-4.6-arm64)GFM_1.2.2.tgz(r-4.5-x86_64)GFM_1.2.2.tgz(r-4.5-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://feiyoung.r-universe.dev', 'https://cloud.r-project.org'))

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factorsopenblascpp

5.86 score 2 stars 3 packages 10 scripts 288 downloads 2 mentions 7 exports 11 dependencies

Last updated from:63d05da037. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK152
linux-devel-x86_64OK141
source / vignettesOK228
linux-release-arm64OK159
linux-release-x86_64OK142
macos-release-arm64OK213
macos-release-x86_64OK278
macos-oldrel-arm64OK116
macos-oldrel-x86_64OK366
windows-develOK166
windows-releaseOK114
windows-oldrelOK124
wasm-releaseOK138

Exports:chooseFacNumberFactormgendatagfmmeasurefunoverdispersedGFMOverGFMchooseFacNumber

Dependencies:codetoolsdoSNOWforeachirlbaiteratorslatticeMASSMatrixRcppRcppArmadillosnow

GFM: A Simple Transcriptomics Data
Load real data | Fit GFM model | Compare with LFM in downstream analysis
Rendered fromGFM.Brain.Rmdusingknitr::rmarkdown

Last update: 2023-08-11
Started: 2021-12-25

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
Rendered fromGFM.Simu.Rmdusingknitr::rmarkdown

Last update: 2023-08-11
Started: 2021-12-25

Installation
Install the GFM
Rendered fromRGFM.Rmdusingknitr::rmarkdown

Last update: 2023-08-11
Started: 2021-12-25

OverGFM: simulated examples
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
Rendered fromOverGFM_exam.Rmdusingknitr::rmarkdown

Last update: 2023-08-11
Started: 2023-08-11