Robust Exponential Factor Analysis
Overview
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. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) doi:10.1016/j.jmva.2025.105567.
- Initial CRAN submission.
From NEWS.md in the release
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 14%1 run in a plain check · 6 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 71%
Downloads
Dependencies
Installing REFA adds 15 packages (8 with compiled code, 12 MB of source tarballs). Heaviest: cubature 3.2 MB, Rcpp 1.8 MB, timeSeries 1.4 MB.
Code & Tests
People & History
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- RR 4.6.0 released · 2026-04-24
- 0.2.0Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.02023-11-19
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-11-19
- Total releases
- 2 / 3 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 5.8 KB
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