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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>.

copied from cf-staging / r-party
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conda 880.9 kB | win-64/r-party-1.3_16-r43h524d87e_0.conda  8 months and 12 days ago 279 main
conda 885.3 kB | win-64/r-party-1.3_16-r44h524d87e_0.conda  8 months and 12 days ago 264 main
conda 887.3 kB | osx-64/r-party-1.3_16-r44hfdf4475_0.conda  8 months and 12 days ago 242 main
conda 878.7 kB | osx-64/r-party-1.3_16-r43hfdf4475_0.conda  8 months and 12 days ago 240 main
conda 887.0 kB | linux-64/r-party-1.3_16-r43h4bc722e_0.conda  8 months and 12 days ago 1024 main
conda 891.0 kB | linux-64/r-party-1.3_16-r44h4bc722e_0.conda  8 months and 12 days ago 975 main

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