Description Details References
FarmTest package 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 (Wang et al, 2020; Ke et al, 2019) to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or assymetric 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, covariance and regression estimators that are of independent interest.
See its GitHub page https://github.com/XiaoouPan/FarmTest for details.
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Bose, K., Fan, J., Ke, Y., Pan, X. and Zhou, W.-X. (2019). FarmTest: An R package for factor-adjusted robust multiple testing, Preprint.
Fan, J., Ke, Y., Sun, Q. and Zhou, W-X. (2019). FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control. J. Amer. Statist. Assoc., 114, 1880-1893.
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