Package: REFA 0.1.0

REFA: Robust Exponential Factor Analysis

A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance.

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

REFA_0.1.0.tar.gz
REFA_0.1.0.zip(r-4.5)REFA_0.1.0.zip(r-4.4)REFA_0.1.0.zip(r-4.3)
REFA_0.1.0.tgz(r-4.4-any)REFA_0.1.0.tgz(r-4.3-any)
REFA_0.1.0.tar.gz(r-4.5-noble)REFA_0.1.0.tar.gz(r-4.4-noble)
REFA_0.1.0.tgz(r-4.4-emscripten)REFA_0.1.0.tgz(r-4.3-emscripten)
REFA.pdf |REFA.html
REFA/json (API)
NEWS

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

Peer review:

On CRAN:

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

1.00 score 144 downloads 7 exports 1 dependencies

Last updated 1 years agofrom:8ec5c41f87. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-winOKNov 14 2024
R-4.5-linuxOKNov 14 2024
R-4.4-winOKNov 14 2024
R-4.4-macOKNov 14 2024
R-4.3-winOKNov 14 2024
R-4.3-macOKNov 14 2024

Exports:ECCest_numFAgendataREFAREFA_FNTR

Dependencies:mvtnorm