Nothing
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# _ #
# | | #
# _ __ ___ __ __ ___ _ __ | | _ _ #
# | '_ \ / _ \ \ \ /\ / / / _ \ | '__| | | | | | | #
# | |_) | | (_) | \ V V / | __/ | | | | | |_| | #
# | .__/ \___/ \_/\_/ \___| |_| |_| \__, | #
# | | __/ | #
# |_| |___/ #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Author: Mihai A. Constantin #
# Contact: mihai@mihaiconstantin.com #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Imports.
#' @importFrom parallel detectCores makeCluster stopCluster clusterExport
#' @importFrom parallel clusterEvalQ parSapply parApply clusterCall
#' @importFrom parallel clusterEvalQ makePSOCKcluster stopCluster
#' @importFrom ggplot2 theme_bw element_line geom_boxplot geom_density
#' @importFrom ggplot2 element_text geom_ribbon scale_fill_manual
#' @importFrom ggplot2 scale_alpha_manual element_rect geom_segment annotate
#' @importFrom ggplot2 coord_cartesian geom_line geom_text ggsave
#' @importFrom ggplot2 scale_x_continuous geom_histogram ggplot stat_ecdf
#' @importFrom ggplot2 geom_vline geom_hline geom_point aes scale_y_continuous
#' @importFrom ggplot2 labs theme margin
#' @importFrom rlang .data .env
#' @importFrom patchwork plot_layout
#' @importFrom R6 R6Class
#' @importFrom bootnet genGGM ggmGenerator
#' @importFrom osqp osqpSettings osqp
#' @importFrom progress progress_bar
#' @importFrom qgraph EBICglasso
#' @importFrom quadprog solve.QP
#' @importFrom splines2 iSpline bSpline
#' @importFrom mvtnorm rmvnorm
#' @include logo.R
#' @title
#' Sample Size Analysis for Psychological Networks and More
#'
#' @description
#' `powerly` is a package that implements the method by [Constantin et al.
#' (2021)](https://psyarxiv.com/j5v7u) for conducting sample size analysis for
#' network models.
#'
#' @details
#' The method implemented is implemented in the main function [powerly()]. The
#' implementation takes the form of a three-step recursive algorithm designed to
#' find an optimal sample size value given a model specification and an outcome
#' measure of interest. It starts with a Monte Carlo simulation step for
#' computing the outcome at various sample sizes. It continues with a monotone
#' curve-fitting step for interpolating the outcome. The final step employs
#' stratified bootstrapping to quantify the uncertainty around the fitted curve.
#'
#' @aliases powerly-package
#'
#' @keywords internal
"_PACKAGE"
# On package attach or load.
.onAttach <- function(libname, pkgname) {
# Only show the logo if this is a human-handled session.
if(interactive()) {
# Print the logo.
packageStartupMessage(LOGO)
}
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.