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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:8ec5c41f87. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 14 2024 |
R-4.5-win | OK | Nov 14 2024 |
R-4.5-linux | OK | Nov 14 2024 |
R-4.4-win | OK | Nov 14 2024 |
R-4.4-mac | OK | Nov 14 2024 |
R-4.3-win | OK | Nov 14 2024 |
R-4.3-mac | OK | Nov 14 2024 |
Exports:ECCest_numFAgendataREFAREFA_FNTR
Dependencies:mvtnorm