--- a
+++ b/partyMod/tests/TreeGrow-regtest.R
@@ -0,0 +1,85 @@
+
+set.seed(290875)
+library("party")
+if (!require("TH.data"))
+    stop("cannot load package TH.data")
+if (!require("coin"))
+    stop("cannot load package coin")
+
+### get rid of the NAMESPACE
+attach(asNamespace("party"))
+
+gtctrl <- new("GlobalTestControl")
+tlev <- levels(gtctrl@testtype)
+
+data(GlaucomaM, package = "TH.data")
+gtree <- ctree(Class ~ ., data = GlaucomaM)
+tree <- gtree@tree
+stopifnot(isequal(tree[[5]][[3]], 0.059))
+predict(gtree)
+
+# print(tree)
+
+stump <- ctree(Class ~ ., data = GlaucomaM, 
+               control = ctree_control(stump = TRUE))
+print(stump)
+
+data(treepipit, package = "coin")
+
+tr <- ctree(counts ~ ., data = treepipit)
+tr
+plot(tr)
+
+
+data(GlaucomaM, package = "TH.data")
+
+tr <- ctree(Class ~ ., data = GlaucomaM)
+tr
+plot(tr)
+
+data(GBSG2, package = "TH.data")  
+
+GBSG2tree <- ctree(Surv(time, cens) ~ ., data = GBSG2)
+GBSG2tree
+plot(GBSG2tree)
+plot(GBSG2tree, terminal_panel = node_surv(GBSG2tree))
+survfit(Surv(time, cens) ~ as.factor(GBSG2tree@where), data = GBSG2)
+names(GBSG2)
+
+tr <- ctree(Surv(time, cens) ~ ., data = GBSG2, 
+            control = ctree_control(teststat = "max", 
+                                    testtype = "Univariate"))
+tr
+plot(tr)
+
+data("mammoexp", package = "TH.data")
+attr(mammoexp$ME, "scores") <- 1:3   
+attr(mammoexp$SYMPT, "scores") <- 1:4
+attr(mammoexp$DECT, "scores") <- 1:3 
+names(mammoexp)[names(mammoexp) == "SYMPT"] <- "symptoms"
+names(mammoexp)[names(mammoexp) == "PB"] <- "benefit"
+
+names(mammoexp)
+tr <- ctree(ME ~ ., data = mammoexp)
+tr
+plot(tr)
+
+treeresponse(tr, newdata = mammoexp[1:5,])
+
+### check different user interfaces
+data("iris")
+x <- as.matrix(iris[,colnames(iris) != "Species"])
+y <- iris[,"Species"]
+newx <- x
+
+ls <- LearningSample(x, y)
+p1 <- unlist(treeresponse(ctree(Species ~ ., data = iris), newdata = as.data.frame(newx)))
+p2 <- unlist(treeresponse(ctreefit(ls, control = ctree_control()), newdata = as.matrix(newx)))
+stopifnot(identical(max(abs(p1 - p2)), 0))
+
+set.seed(29)
+p1 <- unlist(treeresponse(cforestfit(ls, control = cforest_unbiased(mtry = 1)), newdata = as.matrix(newx)))
+set.seed(29)
+p2 <- unlist(treeresponse(cforest(Species ~ ., data = iris, control = cforest_unbiased(mtry = 1)), 
+             newdata = as.data.frame(newx)))
+stopifnot(identical(max(abs(p1 - p2)), 0))