DESCRIPTION

Package: powerly
Title: Sample Size Analysis for Psychological Networks and More
Version: 1.8.6
Authors@R:
    person(given = "Mihai",
           family = "Constantin",
           role = c("aut", "cre"),
           email = "mihai@mihaiconstantin.com",
           comment = c(ORCID = "0000-0002-6460-0107"))
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.
License: MIT + file LICENSE
URL: https://powerly.dev
BugReports: https://github.com/mihaiconstantin/powerly/issues
Imports:
    R6,
    progress,
    parallel,
    splines2,
    quadprog,
    bootnet,
    qgraph,
    ggplot2,
    rlang,
    mvtnorm,
    patchwork
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.1
Collate:
    'Backend.R'
    'Basis.R'
    'Model.R'
    'GgmModel.R'
    'Interpolation.R'
    'Spline.R'
    'StepThree.R'
    'StepTwo.R'
    'Statistic.R'
    'PowerStatistic.R'
    'StatisticFactory.R'
    'ModelFactory.R'
    'StepOne.R'
    'Range.R'
    'Method.R'
    'QuadprogSolver.R'
    'Solver.R'
    'SolverFactory.R'
    'Validation.R'
    'constants.R'
    'exports.R'
    'helpers.R'
    'logo.R'
    'powerly-package.R'
Suggests:
    testthat (>= 3.0.0)
Config/testthat/edition: 3