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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.

Last 30 days

This package has been downloaded 23,555 times in the last 30 days. The downloads are officially high enough to crash an underfunded departmental server. Quite an accomplishment! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 628 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 256,550 times in the last 365 days. That's a whole lot of downloads. Somewhere, a librarian is trying to figure out why more bandwidth is needed. The day with the most downloads was Jan 30, 2025 with 1,565 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.