utils::globalVariables(c("invDRIFT","II","DRIFTexp","vec2diag","diag2vec",
"mxData","mxMatrix","mxAlgebra","MANIFESTVARbase","MANIFESTVARcholdiag",
"MANIFESTVARchol","T0VARbase","T0VARcholdiag","T0VARchol","DIFFUSIONbase",
"DIFFUSIONcholdiag","DIFFUSIONchol","invDRIFTHATCH","cvectorize","DRIFTHATCH",
"TRAITVARbase","TRAITVARcholdiag","TRAITVARchol","MANIFESTTRAITVARbase",
"MANIFESTTRAITVARcholdiag","MANIFESTTRAITVARchol","mxComputeSequence",
"mxComputeGradientDescent","mxComputeReportDeriv","TDPREDVARbase",
"TDPREDVARcholdiag","TDPREDVARchol","TIPREDVARbase","TIPREDVARcholdiag",
"TIPREDVARchol","mxExpectationRAM","mxFitFunctionML","Ilatent","Alatent",
"Amanifestcov","invIminusAlatent","Smanifest","Amanifest","Mmanifest",
"mxExpectationNormal","omxSelectRowsAndCols","expCov","existenceVector",
"omxSelectCols","expMean","log2pi","numVar_i","filteredExpCov","%&%",
"filteredDataRow","filteredExpMean","firstHalfCalc","secondHalfCalc",
"rowResults","mxFitFunctionRow","TdpredNames","discreteCINT_T1","discreteDRIFT_T1",
"discreteDIFFUSION_T1","mxExpectationStateSpace","mxExpectationSSCT","ctsem.fitfunction",
"ctsem.penalties","FIMLpenaltyweight","ctsem.simpleDynPenalty","ieigenval",
"mxFitFunctionAlgebra","mxCI","mxComputeConfidenceInterval","DRIFT",
"n.latent","DIFFUSION","TRAITVAR","n.TDpred","TDPREDEFFECT","TDPREDMEANS",
"TDPREDVAR","TRAITTDPREDCOV","n.TIpred","TIPREDEFFECT","TIPREDMEANS",
"TIPREDVAR","CINT","n.manifest","LAMBDA","MANIFESTMEANS","MANIFESTVAR",
"mxFitFunctionMultigroup", "asymDIFFUSION", 'data.id',
'filteredExpCovchol','filteredExpCovcholinv',
'A','M','testd','ctstantestdat','smfnode',
'T0VAR','T0MEANS', 'MANIFESTTRAITVAR',
'TDpredNames', 'TIpredNames', 'Tpoints', 'extract', 'latentNames', 'manifestNames',
'plot', 'points','T0TRAITEFFECT',
'T0VARsubindex','DRIFTsubindex','DIFFUSIONsubindex','CINTsubindex','.'))
if(1==99){
`:=` = NULL
`.` =NULL
.N = .id = id= . = grp = NULL # due to NSE notes in R CMD check
}
#' ctsemOMX
#'
#' ctsem is an R package for continuous time structural equation modelling of panel (N > 1)
#' and time series (N = 1) data, using either a frequentist or Bayesian approach, or middle
#' ground forms like maximum a posteriori. This ctsemOMX addition includes the original OpenMx based functions
#' which have been split off from the main package.
#'
#' The general workflow begins by specifying a model using the \code{\link{ctModel}} function,
#' in which the \code{type} of model is also specified. Then the model is fit to data using
#' either \code{\link{ctFit}} if the original 'omx' (OpenMx, SEM, max likelihood) model is specified.
#' The omx forms are no longer in
#' development and for most purposes, the newer stan based forms (contained in the base ctsem package)
#' are more robust and flexible.
#' For citation info, please run \code{citation('ctsem')} .
#'
#' @docType package
#' @name ctsemOMX
#' @import ctsem
#' @import grDevices methods stats graphics OpenMx
#' @importFrom plyr aaply alply round_any
#' @importFrom utils relist as.relistable tail capture.output
#'
#' @references
#' https://www.jstatsoft.org/article/view/v077i05
#'
#' Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling.
#' Psychological Methods. Advance online publication.http://dx.doi.org/10.1037/met0000168
#'
NULL
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.