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" . 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" 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.


Reference manual

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2.2.0 by Xiaoou Pan, a year ago


Browse source code at https://github.com/cran/FarmTest

Authors: Xiaoou Pan [aut, cre] , Yuan Ke [aut] , Wen-Xin Zhou [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, graphics

Linking to Rcpp, RcppArmadillo

System requirements: C++11

See at CRAN