FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing

Authors

Koushiki Bose, Princeton University
Jianqing Fan, Princeton University
Yuan Ke, University of Georgia
Xiaoou Pan, University of California, San Diego
Wen-Xin Zhou, University of California, San Diego

Date of this Version

12-2020

Document Type

Article

Citation

The R Journal (December 2020) 12(2); Editor: Michael J. Kane

Comments

Copyright 2020, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

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.