We provide a publicly available library FarmTest in the R programming system. This library implements a factor-adjusted robust multiple testing principle proposed by (Fan et al. 2019) for large-scale simultaneous inference on mean effects. We use a multi-factor model to explicitly capture the dependence among a large pool of variables. Three types of factors are considered: observable, latent, and a mixture of observable and latent factors. The non-factor case, which corresponds to standard multiple mean testing under weak dependence, is also included. The library implements a series of adaptive Huber methods integrated with fast data-driven tuning schemes to estimate model parameters and to construct test statistics that are robust against heavy-tailed and asymmetric error distributions. Extensions to two-sample multiple mean testing problems are also discussed. The results of some simulation experiments and a real data analysis are reported.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-023.zip
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For attribution, please cite this work as
Fan, et al., "FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing", The R Journal, 2021
BibTeX citation
@article{RJ-2021-023,
author = {Fan, Koushiki Bose, Jianqing and Ke, Yuan and Zhou, Xiaoou Pan, Wen-Xin},
title = {FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-023},
doi = {10.32614/RJ-2021-023},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {388-401}
}