Adaptive P-Value Thresholding for Multiple Hypothesis Testing with Side Information
Adaptive P-value Thresholding
Adaptive P-value Thresholding with Generalized Additive Models
Adaptive P-value Thresholding with Generalized Linear Models
Adaptive P-value Thresholding with L1/L2 Penalized Generalized Linear ...
Quantifying Information Loss of Adaptive P-Value Thresholding
Fitting Conditional Two-Groups Models on Unmasked P-Values
adapt_model Objects for M-steps
Generate exp_family Objects for Exponential Families
Plotting Functions for AdaPT with 1D Covariates
Plotting Functions for AdaPT with 2D Covariates
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>.
Useful links