Version 1.0-17 Description 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 sur...
Description This package implements random forest method for model based recursive partitioning. The...
Traditionally, multiple linear regression has been widely used in the field of organizational scienc...
<p>Note: Generic classification model based on training data (n = 2074). The variables in the number...
Version 1.0-13 Description A computational toolbox for recursive partitioning. The core of the packa...
Version 1.0-23 Description A computational toolbox for recursive partitioning. The core of the packa...
The party package (Hothorn, Hornik, and Zeileis 2006) aims at providing a recur-sive part(y)itioning...
Version 1.0-4 Description A toolkit with infrastructure for representing, summarizing, and visualizi...
The partykit package provides a flexible toolkit with infrastructure for learning, rep-resenting, su...
MOB is a generic algorithm for model-based recursive partitioning (Zeileis, Hothorn, and Hornik 2008...
The party package (Hothorn, Hornik, and Zeileis 2006) provides the function mob() im-plementing a re...
Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of...
Recursive partitioning methods have become popular and widely used tools for nonparametric regressio...
Stability aspects of recursive partitioning procedures are investigated. Using resampling techniques...
Binary recursive partitioning (BRP) is a computationally-intensive statistical method that can be us...
Recursive partitioning based on psychometric models,employing the general MOB algo- rithm (from pack...
Description This package implements random forest method for model based recursive partitioning. The...
Traditionally, multiple linear regression has been widely used in the field of organizational scienc...
<p>Note: Generic classification model based on training data (n = 2074). The variables in the number...
Version 1.0-13 Description A computational toolbox for recursive partitioning. The core of the packa...
Version 1.0-23 Description A computational toolbox for recursive partitioning. The core of the packa...
The party package (Hothorn, Hornik, and Zeileis 2006) aims at providing a recur-sive part(y)itioning...
Version 1.0-4 Description A toolkit with infrastructure for representing, summarizing, and visualizi...
The partykit package provides a flexible toolkit with infrastructure for learning, rep-resenting, su...
MOB is a generic algorithm for model-based recursive partitioning (Zeileis, Hothorn, and Hornik 2008...
The party package (Hothorn, Hornik, and Zeileis 2006) provides the function mob() im-plementing a re...
Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of...
Recursive partitioning methods have become popular and widely used tools for nonparametric regressio...
Stability aspects of recursive partitioning procedures are investigated. Using resampling techniques...
Binary recursive partitioning (BRP) is a computationally-intensive statistical method that can be us...
Recursive partitioning based on psychometric models,employing the general MOB algo- rithm (from pack...
Description This package implements random forest method for model based recursive partitioning. The...
Traditionally, multiple linear regression has been widely used in the field of organizational scienc...
<p>Note: Generic classification model based on training data (n = 2074). The variables in the number...