Title: Power Analysis for Longitudinal Multilevel Models
bias, Type I errors and the consequences of model misspecification.
Imports: stats, methods, parallel, lme4 (>= 1.1), Matrix, MASS, scales,
Suggests: testthat, dplyr, tidyr, knitr, rmarkdown, pbmcapply (>= 1.1),
lmerTest (>= 2.0), ggplot2 (>= 2.2), ggsci, viridis, gridExtra,

Description: Calculate power for the 'time x treatment' effect
in two- and three-level multilevel longitudinal studies with missing data.
Both the third-level factor (e.g. therapists, schools, or physicians),
and the second-level factor (e.g. subjects), can be assigned random slopes.
Studies with partially nested designs, unequal cluster sizes,
unequal allocation to treatment arms, and different dropout patterns
per treatment are supported. For all designs power can be

Title: Sample Size Analysis for Psychological Networks and More
Description: An implementation of the sample size computation method for network
models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>
The implementation takes the form of a three-step recursive algorithm
designed to find an optimal sample size given a model specification and a
performance measure of interest. It starts with a Monte Carlo simulation
step for computing the performance measure and a statistic at various sample
sizes selected from an initial sample size range. It continues with a
monotone curve-fitting step for interpolating the statistic across the entire
sample size range. The final step employs stratified bootstrapping to quantify
the uncertainty around the fitted curve.
Imports: R6, progress, parallel, splines2, quadprog, osqp, bootnet,
Collate: 'Backend.R' 'Basis.R' 'Model.R' 'GgmModel.R' 'Interpolation.R'
Author: Mihai Constantin [aut, cre] (<https://orcid.org/0000-0002-6460-0107>)

models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>.

qgraph, ggplot2, rlang, mvtnorm, patchwork

Package: powerpkg
Type: Package
Title: Power analyses for the affected sib pair and the TDT design
Version: 1.2
Date: 2005-07-30
Author: Daniel E. Weeks
Maintainer: Daniel E. Weeks <dweeks@watson.hgen.pitt.edu>
Description: (1) To estimate the power of testing for linkage using an
affected sib pair design, as a function of the recurrence risk ratios. We
will use analytical power formulae as implemented in R. These are based
on a Mathematica notebook created by Martin Farrall. (2) To examine how
the power of the transmission disequilibrium test (TDT) depends on the
disease allele frequency, the marker allele frequency, the strength of
the linkage disequilibrium, and the magnitude of the genetic effect. We
will use an R program that implements the power formulae of Abel and
Muller-Myhsok (1998). These formulae allow one to quickly compute power
of the TDT approach under a variety of different conditions. This R
program was modeled on Martin Farrall's Mathematica notebook.
License: GPL version 2 or newer.
Packaged: Wed Feb  8 12:58:56 2006; hornik