R/data.R

In FredHasselman/casnet: A Toolbox for Studying Complex Adaptive Systems and Networks

#' Random Number Sequences
#'
#' A dataset containing sequences of 100 numbers generated bij 242 participants who were instruted to generate random sequences.
#'
#' @format A data frame with 24200 rows and 3 variables:
#' \describe{
#'   \item{ID}{Participant ID}
#'   \item{time}{Temporal order}
#'   \item{number}{A number between 1 and 9}
#' }
#' @references
#' Oomens, W., Maes, J. H., Hasselman, F., & Egger, J. I. (2015). A time series approach to random number generation: using recurrence quantification analysis to capture executive behavior. Frontiers in human neuroscience, 9
#' @source <https://www.frontiersin.org/articles/10.3389/fnhum.2015.00319/full>
#' @docType data
#' @keywords data
"RNG"


#' Data from the Many Analysts study.
#'
#' @name manyAnalystsESM
#' @docType data
#' @author Bastiaansen et al. (2019). Corresponding author: L.F. Bringmann \email{l.f.bringmann@rug.nl}
#' @references Bastiaansen et al. (2019). Time to get personal? The impact of researchers’ choices on the selection of treatment targets using the experience sampling methodology. [PsyArXiv](https://doi.org/10.31234/osf.io/c8vp7)
#' @references Many Analysts ESM [Data set](https://osf.io/h3djy/)
#' @keywords data
"manyAnalystsESM"


#' Coloured noise data
#'
#' @name ColouredNoise
#' @format A data frame with 1024 rows and 41 columns. Each column represent a coloured noise series with spectral slopes ranging from -2 to 2.
#' @docType data
#' @keywords data
"ColouredNoise"