The R Journal: article published in 2020, volume 12:2

FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing PDF download
Koushiki Bose, Jianqing Fan, Yuan Ke, Xiaoou Pan and Wen-Xin Zhou , The R Journal (2020) 12:2, pages 372-387.

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.

Received: 2020-06-03; online 2021-01-15, supplementary material, (429.6 Kb)
CRAN packages: FarmTest, Rcpp, multcomp, mutoss, rstiefel
CRAN Task Views implied by cited CRAN packages: Bayesian, ClinicalTrials, HighPerformanceComputing, NumericalMathematics, SocialSciences, Survival
Bioconductor packages: qvalue, multtest

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Koushiki Bose and Jianqing Fan and Yuan Ke and Xiaoou Pan
          and Wen-Xin Zhou},
  title = {{FarmTest: An R Package for Factor-Adjusted Robust Multiple
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-023},
  url = {},
  pages = {372--387},
  volume = {12},
  number = {2}