Authors@R: c(person("Housen", "Li", role = "aut", email = "housen.li@outlook.com"),
person("Hannes", "Sieling", role = "aut", email = "hsielin@uni-goettingen.de"),
person("Timo", "Aspelmeier", role = "cre", email = "timo.aspelmeier@mathematik.uni-goettingen.de"))

Title: Estimate, Plot, and Summarize False Discovery Rates
person("Megan", "Murray", email = "megan.c.hollister@vanderbilt.edu", role = c("aut", "cre")),
person("Jeffrey", "Blume", email = "j.blume@vanderbilt.edu", role = c("aut"))
Description: The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines. Megan H. Murray and Jeffrey D. Blume (2020) <arXiv:2010.04680>.
Packaged: 2020-11-10 17:41:09 UTC; meganhollister

Description: The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines.
Packaged: 2022-04-01 16:14:22 UTC; meganhollister

Author: James G. Scott, with contributions from Rob Kass and Jesse Windle
Maintainer: James G. Scott <james.scott@mccombs.utexas.edu>
Description: Tools for FDR problems, including false discovery rate regression.
See corresponding paper: "False discovery rate regression: application to
neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
C. Kelly, Matthew A. Smith, Robert E. Kass.

Title: Compute Sample Size that Meets Requirements for Average Power
Description: Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.