# skip parallel tests on CRAN
skip_on_cran()

if (require("future")) {
  require("semtree")
  data(lgcm)
  
  
  future::plan(multisession, workers = 5)
  
  lgcm$agegroup <- ordered(lgcm$agegroup)
  lgcm$training <- factor(lgcm$training)
  lgcm$noise <- as.numeric(lgcm$noise)
  
  # LOAD IN OPENMX MODEL.
  # A SIMPLE LINEAR GROWTH MODEL WITH 5 TIME POINTS FROM SIMULATED DATA
  
  manifests <- names(lgcm)[1:5]
  lgcModel <- mxModel(
    "Linear Growth Curve Model Path Specification",
    type = "RAM",
    manifestVars = manifests,
    latentVars = c("intercept", "slope"),
    # residual variances
    mxPath(
      from = manifests,
      arrows = 2,
      free = TRUE,
      values = c(1, 1, 1, 1, 1),
      labels = c(
        "residual1",
        "residual2",
        "residual3",
        "residual4",
        "residual5"
      )
    ),
    # latent variances and covariance
    mxPath(
      from = c("intercept", "slope"),
      connect = "unique.pairs",
      arrows = 2,
      free = TRUE,
      values = c(1, 1, 1),
      labels = c("vari", "cov", "vars")
    ),
    # intercept loadings
    mxPath(
      from = "intercept",
      to = manifests,
      arrows = 1,
      free = FALSE,
      values = c(1, 1, 1, 1, 1)
    ),
    # slope loadings
    mxPath(
      from = "slope",
      to = manifests,
      arrows = 1,
      free = FALSE,
      values = c(0, 1, 2, 3, 4)
    ),
    # manifest means
    mxPath(
      from = "one",
      to = manifests,
      arrows = 1,
      free = FALSE,
      values = c(0, 0, 0, 0, 0)
    ),
    # latent means
    mxPath(
      from = "one",
      to = c("intercept", "slope"),
      arrows = 1,
      free = TRUE,
      values = c(1, 1),
      labels = c("meani", "means")
    ),
    mxData(lgcm, type = "raw")
  )
  
  #tree <- semtree(model=lgcModel, data=lgcm)
  
  #lgcModel=mxRun(lgcModel)
  
  # TREE CONTROL OPTIONS.
  # TO OBTAIN BASIC/DEFAULT SMETREE OPTIONS, SIMPLY TPYE THE FOLLOWING:
  
  ctrl <- semtree.control(method = "score", verbose = TRUE)
  
  # RUN TREE.
  
  forest <- semforest(
    model = lgcModel,
    data = lgcm,
    control =
      semforest.control(
        num.trees = 30,
        control = semtree.control(alpha =
                                    1, method = "score")
      ),
    constraints = semtree.constraints(focus.parameter =
                                        "meani")
  )
  
  vim_naive <- varimp(forest)
  
  vim <- varimp(forest, method = "permutationFocus")
  
  #plot(vim)
  #plot(vim_naive)
  
 
  
}