Title: Basic functions for power analysis
Description: Power analysis functions along the lines of Cohen (1988)
Packaged: Wed Feb  1 10:16:27 2006; hornik

Title: Basic Functions for Power Analysis
Description: Power analysis functions along the lines of Cohen (1988).
Maintainer: Helios De Rosario <helios.derosario@gmail.com>

Packaged: Thu Feb  1 14:02:13 2007; hornik

Suggests: ggplot2, scales, knitr, rmarkdown

Title: Power and Sample Size Analysis for One-way and Two-way ANOVA
Author: Pengcheng Lu, Junhao Liu, Devin Koestler
Description: User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.

Title: Power Analyses for Common Designs (Power to the People)
Description: Statistical power analysis for designs including t-tests, correlations,
multiple regression, ANOVA, mediation, and logistic regression. Functions accompany
Packaged: 2019-06-11 03:40:53 UTC; Chris Aberson

Authors@R: person("William", "Cha", email = "william.minseuk.cha@gmail.com",
Description: Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give
biased results due to using the pooled standard deviation, unlike the Welch's t-test.

Description: This is a package for calculating power and sample size
in multiple testing situations using the Benjamini-Hochberg (BH)
false discovery rate (FDR) procedure. The package computes power
and sample size in one of either two ways, using the average
power or the lambda-power. See Izmirlian, G. (2018) <arXiv:1801.03989>.

Package: pwrFDR
Version: 2.8.4
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: 2020-09-25 21:35:01 UTC; izmirlig
Repository: CRAN
Date/Publication: 2020-09-26 06:00:03 UTC