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.

James G. Scott, with contributions from Rob Kass and Jesse Windle
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.

Compute Sample Size that Meets Requirements for Average Power
and FDR
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.

False Discovery Exceedance Controlling Multiple Testing
Procedures
c(person("Sebastian", "Döhler", role = "aut"), person("Florian","Junge", role = c("aut", "cre"), email = "florian.junge@h-da.de"), person("Etienne", "Roquain", role = "ctb"))
Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2019) <arXiv:1912.04607v1>. The main algorithms of the paper are available as continuous, discrete and weighted versions.
Rcpp (>= 1.0.3), methods, PoissonBinomial, pracma
Sebastian Döhler [aut],
Florian Junge [aut, cre],
Etienne Roquain [ctb]

Rcpp (>= 1.0.3), methods, PoissonBinomial (>= 1.1.2), pracma

Rcpp (>= 1.0.3), methods, PoissonBinomial (>= 1.2.0), pracma

'FDboost.R' 'baselearners.R' 'crossvalidation.R' 'methods.R'
'bkroneckerTrafo.R' 'utilityFunctions.R'

Regression models for functional data, i.e. scalar-on-function,
function-on-scalar and function-on-function regression models are fitted
using a component-wise gradient boosting algorithm.
'FDboost.R' 'baselearners.R' 'crossvalidation.R'
'internalsMboost.R' 'methods.R' 'utilityFunctions.R'