--- a
+++ b/partyMod/R/crossvalidation.R
@@ -0,0 +1,60 @@
+
+# library("mboost")
+if (FALSE) {
+cvrisk2 <- function(object, folds = cv(object@weights), 
+    mincriterion = seq(from = 0.6, to = 0.95, by = 0.05),
+    papply = if (require("multicore")) mclapply else lapply,
+    ...){
+
+    weights <- object@weights
+
+    if (any(weights == 0))
+        warning("zero weights")
+    if (is.null(folds)) {
+        folds <- rmultinom(25, length(weights), weights/sum(weights))
+    } else {
+        stopifnot(is.matrix(folds) && nrow(folds) == length(weights))
+    }
+    fitfct <- object@update
+    oobrisk <- matrix(0, nrow = ncol(folds), ncol = length(grid))
+
+    dummyfct <- function(weights) {
+        mod <- fitfct(weights = weights)
+        sapply(mincriterion, function(m) {
+            p <- predict(mod, mincriterion = m)
+            if (is.factor(p)) 
+                err <- p != mod@responses@variables[[1]]
+            if (is.numeric(p))
+                err <- (p - mod@responses@variables[[1]])^2
+            mean(err[weights == 0])
+        })
+    }
+
+    OOBweights <- matrix(rep(weights, ncol(folds)), ncol = ncol(folds))
+    OOBweights[folds > 0] <- 0
+    oobrisk <- papply(1:ncol(folds),
+        function(i) dummyfct(weights = folds[, i]))
+
+    oobrisk <- t(as.data.frame(oobrisk))
+    oobrisk <- oobrisk / colSums(OOBweights)
+    colnames(oobrisk) <- mincriterion
+    rownames(oobrisk) <- 1:nrow(oobrisk)
+    oobrisk
+}
+
+set.seed(290875)
+     
+### regression
+airq <- subset(airquality, !is.na(Ozone))
+airct <- ctree(Ozone ~ ., data = airq, 
+               controls = ctree_control(maxsurrogate = 3, mincriterion = 0.5))
+### bootstrap
+cvm <- cvrisk2(airct, mincriterion = 50:95/100)
+boxplot(cvm)
+
+### 10-fold CV
+cvm <- cvrisk2(airct, mincriterion = 50:95/100, cv(airct@weights, type = "kfold"))
+boxplot(cvm)
+
+}
+