Package: pwrFDR
Version: 2.8.9
Title: FDR Power
Author: Grant Izmirlian Jr <izmirlig@mail.nih.gov>
Maintainer: Grant Izmirlian Jr <izmirlig@mail.nih.gov>
Imports: methods, stats
Description: This is a package for calculating two differing notions of power, or deriving sample
sizes for specified requisite power in multiple testing experiments under a variety of methods for
control of the distribution of the False Discovery Proportion (FDP). More specifically, one can
choose to control the FDP distribution according to control of its (i) mean, e.g. the usual BH-FDR
procedure, or via the probability that it exceeds a given value, delta, via (ii) the Romano
procedure, or via (iii) my procedure based upon asymptotic approximation. Likewise, we can think of
the power in multiple testing experiments in terms of a summary of the distribution of the
True Positive Proportion (TPP). The package will compute power, sample size or any other missing
parameter required for power based upon (i) the mean of the TPP which is the average power
(ii) the probability that the TPP exceeds a given value, lambda, via my asymptotic approximation
procedure. The theoretical results are described in Izmirlian, G. (2020) Stat. and Prob. letters,
<doi:10.1016/j.spl.2020.108713>, and an applied  paper describing the methodology with a
simulation study is in preparation.
Encoding: UTF-8
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2021-05-12 15:03:32 UTC; izmirlig
Repository: CRAN
Date/Publication: 2021-05-12 16:10:03 UTC

Title: Statistical Power Simulation for Testing the Rasch Model
Description: Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
Suggests: eRm, roxygen2, utils, testthat

Title: Power and Sample Size Calculation Tools
Authors@R: c(person("Metin", "Bulus", email = "bulusmetin@gmail.com", role = c("aut", "cre")))
Description: Statistical power and minimum required sample size calculations for  (1) testing a proportion (one-sample) against a constant,  (2) testing a mean (one-sample) against a constant,  (3) testing difference between two proportions (independent samples),  (4) testing difference between two means (independent and paired samples),  (5) testing a correlation (one-sample) against a constant,  (6) testing difference between two correlations (independent samples),  (7) testing an R-squared against zero in linear regression, (8) testing an R-squared difference against zero in hierarchical regression,  (9) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of variance (could be one-way, two-way, and three-way),  (10) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of covariance (could be one-way, two-way, and three-way),  (11) testing an eta-squared or f-squared (for between, within, and interaction effects) against zero in one-way repeated measures analysis of variance (with non-sphericity correction and repeated measures correlation). Alternative hypothesis can be formulated as "not equal", "less", "greater", "non-inferior", "superior", or "equivalent" in (1), (2), (3), and (4); as "not equal", "less", or "greater" in (5) and (6); but always as "greater" in (7), (8), (9), (10), and (11). Reference: Bulus & Polat (2022) <https://osf.io/download/sx3rk/>.