Package: GrFA 0.2

GrFA: Group Factor Analysis

Several group factor analysis algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008> , Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and Aggregated projection method.

Authors:Jiaqi Hu [cre, aut], Ting Li [aut], Xueqin Wang [aut]

GrFA_0.2.tar.gz
GrFA_0.2.zip(r-4.5)GrFA_0.2.zip(r-4.4)GrFA_0.2.zip(r-4.3)
GrFA_0.2.tgz(r-4.4-any)GrFA_0.2.tgz(r-4.3-any)
GrFA_0.2.tar.gz(r-4.5-noble)GrFA_0.2.tar.gz(r-4.4-noble)
GrFA_0.2.tgz(r-4.4-emscripten)GrFA_0.2.tgz(r-4.3-emscripten)
GrFA.pdf |GrFA.html
GrFA/json (API)

# Install 'GrFA' in R:
install.packages('GrFA', repos = c('https://jiaqihu2021.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

8 exports 0.36 score 1 dependencies 170 downloads

Last updated 24 days agofrom:c9928fc10b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 25 2024
R-4.5-winOKAug 25 2024
R-4.5-linuxOKAug 25 2024
R-4.4-winOKAug 25 2024
R-4.4-macOKAug 25 2024
R-4.3-winOKAug 25 2024
R-4.3-macOKAug 25 2024

Exports:APMCCACPest_numFAGCCgendataTraceRatio

Dependencies:mvtnorm