FarmTest: Factor-Adjusted Robust Multiple Testing

Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>. Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.

Version: 2.2.0
Depends: R (≥ 3.6.0)
Imports: Rcpp, graphics
LinkingTo: Rcpp, RcppArmadillo
Published: 2020-09-07
Author: Xiaoou Pan [aut, cre], Yuan Ke [aut], Wen-Xin Zhou [aut]
Maintainer: Xiaoou Pan <xip024 at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: C++11
Materials: README
CRAN checks: FarmTest results


Reference manual: FarmTest.pdf
Package source: FarmTest_2.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: FarmTest_2.2.0.tgz, r-oldrel: FarmTest_2.2.0.tgz
Old sources: FarmTest archive


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