## ---- eval=F------------------------------------------------------------------
#  fitMPT <- traitMPT(
#    eqnfile = "2htm.txt",
#    data = "data_ind.csv",
#    restrictions = list("Dn=Do", "g=.5"),
#    covData = "data_covariates.csv",
#    corProbit = TRUE,
#    predStructure = list("Do ; IQ"), # IQ as predictor for Do=Dn
#    ...
#  )

## ---- eval = FALSE------------------------------------------------------------
#  fitMPT <- traitMPT(
#    eqnfile = "2htm.txt",
#    data = "data_ind.csv",
#    covData = "data_covariates.csv",
#    predStructure = list(
#      "Do ; factor1",
#      "Dn ; factor2"
#    ), # discrete factors
#    predType = c("c", "c", "f", "r")
#  )

## ---- eval=F------------------------------------------------------------------
#  getGroupMeans(fitMPT)

## ---- eval=FALSE--------------------------------------------------------------
#  transformedParameters <- list(
#    "deltaG = G_1-G_2", # difference of parameters
#    "G1_larger = G_1>G_2"
#  ) # Bayesian p-value / testing order constraints

## ---- eval=FALSE--------------------------------------------------------------
#  # beta-MPT
#  genBeta <- genBetaMPT(
#    N = 100, # number of participants
#    numItems = c(Target = 250, Lure = 250), # number of responses per tree
#    eqnfile = "2htm.eqn", # path to MPT file
#    mean = c(Do = .7, Dn = .7, g = .5), # true group-level parameters
#    sd = c(Do = .1, Dn = .1, g = .05)
#  ) # SD of individual parameters
#  
#  # latent-trait MPT
#  genTrait <- genTraitMPT(
#    N = 100, # number of participants
#    numItems = c(Target = 250, Lure = 250), # number of responses per tree
#    eqnfile = "2htm.eqn", # path to MPT file
#    mean = c(Do = .7, Dn = .7, g = .5), # true group-level parameters
#    sigma = c(Do = .25, Dn = .25, g = .05), # SD of latent (!) individual parameters
#    rho = diag(3)
#  ) # correlation matrix. here: no correlation