Author: Xiaoou Pan, Yuan Ke and Wen-Xin Zhou
Description: Performs robust multiple testing for means in the presence of known and unknown latent factors.
It implements a series of adaptive Huber methods combined with fast data-drive tuning schemes to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymetric error distributions.
Extensions to two-sample simultaneous mean comparison problems are also included.
As by-products, this package also contains functions that compute adaptive Huber mean and covariance matrix estimators that are of independent interest.

Authors@R: c(person("Xiaoou", "Pan", email = "xip024@ucsd.edu", role = c("aut", "cre")),
person("Yuan", "Ke", email = "yuan.ke@uga.edu", role = "aut"),
person("Wen-Xin", "Zhou", email = "wez243@ucsd.edu", role = "aut"))
Description: Performs robust multiple testing for means in the presence of known and unknown latent factors (Fan et al., 2019).
It implements a series of adaptive Huber methods combined with fast data-drive tuning schemes (Ke et al., 2019) to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions.

Title: Factor-Adjusted Robust Multiple Testing
Description: 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.
As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.