--- a
+++ b/partyMod/man/ctree_control.Rd
@@ -0,0 +1,71 @@
+\name{Control ctree Hyper Parameters}
+\alias{ctree_control}
+\title{ Control for Conditional Inference Trees }
+\description{
+
+  Various parameters that control aspects of the `ctree' fit.
+
+}
+\usage{
+ctree_control(teststat = c("quad", "max"), 
+              testtype = c("Bonferroni", "MonteCarlo", 
+                           "Univariate", "Teststatistic"), 
+              mincriterion = 0.95, minsplit = 20, minbucket = 7, 
+              stump = FALSE, nresample = 9999, maxsurrogate = 0, 
+              mtry = 0, savesplitstats = TRUE, maxdepth = 0)
+}
+\arguments{
+  \item{teststat}{ a character specifying the type of the test statistic
+                       to be applied. }
+  \item{testtype}{ a character specifying how to compute the distribution of
+                   the test statistic. }
+  \item{mincriterion}{ the value of the test statistic (for \code{testtype == "Teststatistic"}),
+                       or 1 - p-value (for other values of \code{testtype}) that
+                       must be exceeded in order to implement a split. }
+  \item{minsplit}{ the minimum sum of weights in a node in order to be considered
+                   for splitting. }
+  \item{minbucket}{ the minimum sum of weights in a terminal node. }
+  \item{stump}{ a logical determining whether a stump (a tree with three
+                nodes only) is to be computed. }
+  \item{nresample}{ number of Monte-Carlo replications to use when the
+                    distribution of the test statistic is simulated.}
+  \item{maxsurrogate}{ number of surrogate splits to evaluate. Note the
+                       currently only surrogate splits in ordered
+                       covariables are implemented. }
+  \item{mtry}{ number of input variables randomly sampled as candidates 
+               at each node for random forest like algorithms. The default
+               \code{mtry = 0} means that no random selection takes place.}
+  \item{savesplitstats}{ a logical determining if the process of standardized
+                         two-sample statistics for split point estimate
+                         is saved for each primary split.}
+  \item{maxdepth}{ maximum depth of the tree. The default \code{maxdepth = 0}
+                   means that no restrictions are applied to tree sizes.}
+}
+\details{
+
+  The arguments \code{teststat}, \code{testtype} and \code{mincriterion}
+  determine how the global null hypothesis of independence between all input
+  variables and the response is tested (see \code{\link{ctree}}). The 
+  argument \code{nresample} is the number of Monte-Carlo replications to be
+  used when \code{testtype = "MonteCarlo"}.
+
+  A split is established when the sum of the weights in both daugther nodes
+  is larger than \code{minsplit}, this avoids pathological splits at the
+  borders. When \code{stump = TRUE}, a tree with at most two terminal nodes
+  is computed.
+
+  The argument \code{mtry > 0} means that a random forest like `variable
+  selection', i.e., a random selection of \code{mtry} input variables, is
+  performed in each node.
+
+  It might be informative to look at scatterplots of input variables against
+  the standardized two-sample split statistics, those are available when
+  \code{savesplitstats = TRUE}. Each node is then associated with a vector
+  whose length is determined by the number of observations in the learning
+  sample and thus much more memory is required.
+
+}
+\value{
+  An object of class \code{\link{TreeControl}}.
+}
+\keyword{misc}