Robust Exponential Factor Analysis R package REFA version 0.2.0 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>. Software Depends: R (>= 3.5.0) Imports: fMultivar Jiaqi Hu <hujiaqi@mail.ustc.edu.cn> Comprehensive R Archive Network (CRAN) Jiaqi Hu [cre, aut], Xueqin Wang [aut] GPL (>= 3) 2025-12-07 application/tgz https://CRAN.R-project.org/package=REFA doi:10.32614/CRAN.package.REFA