adaptMT

R package adaptMT: Adaptive P-Value Thresholding for Multiple Hypothesis Testing with Side Information. Implementation of adaptive p-value thresholding (AdaPT), including both a framework that allows the user to specify any algorithm to learn local false discovery rate and a pool of convenient functions that implement specific algorithms. See Lei, Lihua and Fithian, William (2016) <arXiv:1609.06035>.

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References in zbMATH (referenced in 7 articles , 1 standard article )

Showing results 1 to 7 of 7.
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  1. Cao, Hongyuan; Wu, Wei Biao: Testing and estimation for clustered signals (2022)
  2. Cui, Tingting; Wang, Pengfei; Zhu, Wensheng: Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models (2021)
  3. Duan, Boyan; Ramdas, Aaditya; Balakrishnan, Sivaraman; Wasserman, Larry: Interactive martingale tests for the global null (2020)
  4. Katsevich, Eugene; Ramdas, Aaditya: Simultaneous high-probability bounds on the false discovery proportion in structured, regression and online settings (2020)
  5. Durand, Guillermo: Adaptive (p)-value weighting with power optimality (2019)
  6. Li, Ang; Barber, Rina Foygel: Multiple testing with the structure-adaptive Benjamini-Hochberg algorithm (2019)
  7. Lei, Lihua; Fithian, William: AdaPT: an interactive procedure for multiple testing with side information (2018)