lsttheory: Latent State-Trait Theory

## These "global" variables are used in subset() or dplyr commands and are
## mistakenly classified as global variables by R CMD Check. Therefore they
## are added here as "global" variables to avoid the NOTE.
utils::globalVariables(c("est", "gamma_t_equiv", "label", "lhs",
                         "manifest_thetacovariates", "op"))

#' lsttheory
#' 
#' Compute several models of latent state-trait theory..
#'
#' @name lsttheory
#' @aliases lsttheory-package
#' @docType package
NULL


#' Dataset d_taucong.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y1.
#'   \item y2.
#'   \item y3.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 300 rows and 3 variables
#' @name d_taucong
NULL


#' Dataset d_multitraitmultistate02.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y12.
#'   \item y22.
#'   \item y13.
#'   \item y23.
#'   \item y14.
#'   \item y24.
#'   \item x.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 1000 rows and 9 variables
#' @name d_multitraitmultistate02
NULL



#' Dataset d_multitraitmultistate.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y12.
#'   \item y22.
#'   \item y13.
#'   \item y23.
#'   \item y14.
#'   \item y24.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 1000 rows and 8 variables
#' @name d_multitraitmultistate
NULL



#' Dataset d_multistate02.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y31.
#'   \item y12.
#'   \item y22.
#'   \item y32.
#'   \item y13.
#'   \item y23.
#'   \item y33.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 1000 rows and 9 variables
#' @name d_multistate02
NULL



#' Dataset d_multistate.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y12.
#'   \item y22.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 400 rows and 4 variables
#' @name d_multistate
NULL



#' Dataset happy
#' 
#' Restructured wide-format data from Weiss et al. (2021), containing the items 
#' happiness ("How happy do you feel at the moment") and 
#' life satisfaction ("How satisfied are you with your life at the moment?") on five-point Likert scales.
#' Data was assessed 5 times a day on 5 days during an experience-sampling study.
#' The original dataset is available online at: https://osf.io/kwp6n/ 
#' The variables are:
#' 
#' \itemize{
#'   \item happy_1.
#'   \item satisfaction_1.
#'   \item happy_2.
#'   \item satisfaction_2.
#'   \item \cdot
#'   \item happy_25.
#'   \item satisfaction_25.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 425 rows and 50 variables
#' @name happy
NULL



#' Dataset happybig5
#' 
#' Restructured wide-format data from Weiss et al. (2021), containing the items 
#' happiness ("How happy do you feel at the moment") and 
#' life satisfaction ("How satisfied are you with your life at the moment?") on five-point Likert scales.
#' Data was assessed 5 times a day on 5 days during an experience-sampling study.
#' Additionally, this dataset contains measures of the Big Five personality dimensions from an intake session before the experience sampling phase.
#' This dataset can be used to demonstrate how covariates (Big Five) contribute to trait components of Latent State-Trait models. 
#' The original dataset is available online at: https://osf.io/kwp6n/ 
#' The variables are:
#' 
#' \itemize{
#'   \item happy_1.
#'   \item satisfaction_1.
#'   \item happy_2.
#'   \item satisfaction_2.
#'   \item \cdot
#'   \item happy_25.
#'   \item satisfaction_25.
#'   \item Big5_OE_M
#'   \item Big5_CO_M
#'   \item Big5_EX_M
#'   \item Big5_AG_M
#'   \item Big5_ES_M
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 425 rows and 55 variables
#' @name happybig5
NULL