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.

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. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.

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.

Estimates both tail area-based false 
   discovery rates (Fdr) as well as local false discovery rates (fdr) for a 
   variety of null models (p-values, z-scores, correlation coefficients,
   t-scores).  The proportion of null values and the parameters of the null 
   distribution are adaptively estimated from the data.  In addition, the package 
   contains functions for non-parametric density estimation (Grenander estimator), 
   for monotone regression (isotonic regression and antitonic regression with weights),
   for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), 
   for the half-normal and correlation distributions, and for computing
   empirical higher criticism (HC) scores and the corresponding decision threshold.

Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.

These functions were developed to support statistical analysis on functional covariance operators.
  The package contains functions to:
  - compute 2-Wasserstein distances between Gaussian Processes as in
    Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
  - compute the Wasserstein barycenter (Frechet mean) as in Masarotto,
    Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
  - perform analysis of variance testing procedures for functional
    covariances and tangent space principal component analysis of
    covariance operators as in Masarotto, Panaretos & Zemel (2022)
    <arXiv:2212.04797>.
  - perform a soft-clustering based on the Wasserstein distance where
    functional data are classified based on their covariance structure
    as in Masarotto & Masarotto (2023) <doi:10.1111/sjos.12692>.