| PackageDescription | A computational toolbox for recursive partitioning. The core of the
package is ctree(), an implementation of conditional inference trees
which embed tree-structured regression models into a well defined
theory of conditional inference procedures. This non-parametric class
of regression trees is applicable to all kinds of regression problems,
including nominal, ordinal, numeric, censored as well as multivariate
response variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on parametric
models (e.g. linear models, GLMs or survival regression) employing
parameter instability tests for split selection. Extensible
functionality for visualizing tree-structured regression models is
available. The methods are described in Hothorn et al. (2006)
<doi:10.1198/106186006X133933>, Zeileis et al. (2008)
<doi:10.1198/106186008X319331> and Strobl et al. (2007)
<doi:10.1186/1471-2105-8-25>. |