--- a
+++ b/partyMod/tests/Distributions.R
@@ -0,0 +1,117 @@
+
+set.seed(290875)
+library("party")
+if (!require("mvtnorm"))
+    stop("cannot load package mvtnorm")
+
+
+### get rid of the NAMESPACE
+attach(asNamespace("party"))
+
+### 
+###
+###    Regression tests for conditional distributions
+###    
+###    functions defined in file `./src/Distributions.c'
+
+### chisq-distribution of quadratic forms
+t <- 2.1
+df <- 2
+storage.mode(t) <- "double"
+storage.mode(df) <- "double"
+stopifnot(isequal(1 - pchisq(t, df = df), ### P-values!!!
+          .Call("R_quadformConditionalPvalue", t, df, PACKAGE = "party")))
+
+stopifnot(isequal(2*pnorm(-t), 
+          .Call("R_maxabsConditionalPvalue", t, matrix(1), as.integer(1), 0.0, 0.0, 0.0, PACKAGE = "party")))
+
+
+maxpts <- 25000
+storage.mode(maxpts) <- "integer"
+abseps <- 0.0001
+releps <- 0
+tol <- 1e-10
+
+a <- 1.96
+b <- diag(2)
+
+p1 <- .Call("R_maxabsConditionalPvalue", a, b, maxpts, abseps, releps, tol, PACKAGE = "party")
+p2 <- pmvnorm(lower = rep(-a,2), upper = rep(a,2), corr = b)
+stopifnot(isequal(round(p1, 3), round(1 - p2, 3)))
+
+b <- diag(4)
+p1 <- .Call("R_maxabsConditionalPvalue", a, b, maxpts, abseps, releps, tol, PACKAGE = "party")
+p2 <- pmvnorm(lower = rep(-a,4), upper = rep(a,4), corr = b)
+stopifnot(isequal(round(p1, 3), round(1 - p2, 3)))
+
+b <- diag(4)
+b[upper.tri(b)] <- c(0.1, 0.2, 0.3)
+b[lower.tri(b)] <- t(b)[lower.tri(b)]
+p1 <- .Call("R_maxabsConditionalPvalue", a, b, maxpts, abseps, releps, tol, PACKAGE = "party")
+p2 <- pmvnorm(lower = rep(-a,4), upper = rep(a,4), corr = b)
+stopifnot(isequal(round(p1, 3), round(1 - p2, 3)))
+
+### Monte-Carlo approximation of P-Values, univariate
+mydata = data.frame(y = gl(2, 50), x1 = rnorm(100),  
+                    x2 = rnorm(100), x3 = rnorm(100))
+inp <- initVariableFrame(mydata[,"x1",drop = FALSE], trafo = function(data)
+ptrafo(data, numeric_trafo = rank))
+resp <- initVariableFrame(mydata[,"y",drop = FALSE], trafo = NULL, response = TRUE)
+ls <- new("LearningSample", inputs = inp, responses = resp,
+          weights = rep(1, inp@nobs), nobs = nrow(mydata),
+          ninputs = inp@ninputs)
+tm <- ctree_memory(ls)
+varctrl <- new("VariableControl")
+varctrl@teststat <- factor("max", levels = c("max", "quad"))
+varctrl@pvalue <- FALSE
+gtctrl <- new("GlobalTestControl")
+gtctrl@testtype <- factor("MonteCarlo", levels = levels(gtctrl@testtype))
+gtctrl@nresample <- as.integer(19999)
+
+pvals <- .Call("R_GlobalTest", ls, ls@weights, tm, varctrl, gtctrl, PACKAGE = "party")
+wstat <- abs(qnorm(wilcox.test(x1 ~ y, data = mydata, 
+             exact = FALSE, correct = FALSE)$p.value/2))
+wpval <- wilcox.test(x1 ~ y, data = mydata, exact = TRUE)$p.value
+stopifnot(isequal(wstat, pvals[[1]]))
+stopifnot(abs(wpval - (1 - pvals[[2]])) < 0.01)
+
+### Monte-Carlo approximations of P-Values, multiple inputs
+mydata = data.frame(y = gl(2, 50), x1 = rnorm(100),  
+                    x2 = rnorm(100), x3 = rnorm(100))
+inp <- initVariableFrame(mydata[,c("x1", "x2", "x3"),
+                                drop = FALSE], trafo = function(data)
+ptrafo(data, numeric_trafo = rank))
+resp <- initVariableFrame(mydata[,"y",drop = FALSE], trafo = NULL, response = TRUE)
+ls <- new("LearningSample", inputs = inp, responses = resp,
+          weights = rep(1, inp@nobs), nobs = nrow(mydata),
+          ninputs = inp@ninputs)
+tm <- ctree_memory(ls)
+varctrl <- new("VariableControl")
+varctrl@teststat <- factor("max", levels = c("max", "quad"))
+varctrl@pvalue <- TRUE
+gtctrl <- new("GlobalTestControl")
+gtctrl@testtype <- factor("Univariate", levels = levels(gtctrl@testtype))
+gtctrl@nresample <- as.integer(19999)
+
+pvals <- .Call("R_GlobalTest", ls, ls@weights, tm, varctrl, gtctrl, PACKAGE = "party")
+wstat <- c(abs(qnorm(wilcox.test(x1 ~ y, data = mydata, 
+               exact = FALSE, correct = FALSE)$p.value/2)),
+           abs(qnorm(wilcox.test(x2 ~ y, data = mydata, 
+               exact = FALSE, correct = FALSE)$p.value/2)),
+           abs(qnorm(wilcox.test(x3 ~ y, data = mydata, 
+               exact = FALSE, correct = FALSE)$p.value/2)))
+wpval <- c(wilcox.test(x1 ~ y, data = mydata, 
+               exact = FALSE, correct = FALSE)$p.value,
+           wilcox.test(x2 ~ y, data = mydata, 
+               exact = FALSE, correct = FALSE)$p.value,
+           wilcox.test(x3 ~ y, data = mydata, 
+               exact = FALSE, correct = FALSE)$p.value)
+stopifnot(isequal(wstat, pvals[[1]]))
+stopifnot(isequal(wpval, 1 - pvals[[2]]))
+
+### Monte-Carlo approximations of P-Values, min-P approach
+gtctrl@testtype <- factor("MonteCarlo", levels = levels(gtctrl@testtype))
+gtctrl@nresample <- as.integer(19999)
+pvals <- .Call("R_GlobalTest", ls, ls@weights, tm, varctrl, gtctrl, PACKAGE = "party")
+stopifnot(isequal(wstat, pvals[[1]]))
+stopifnot(all(wpval < (1 - pvals[[2]])))