SCM

R Development Page

Contributed R Packages

Below is a list of all packages provided by project partykit: Recursive Partytioning Toolkit.

Important note for package binaries: R-Forge provides these binaries only for the most recent version of R, but not for older versions. In order to successfully install the packages provided on R-Forge, you have to switch to the most recent version of R or, alternatively, install from the package sources (.tar.gz).

Packages

ATR

Alternative Tree Representation

  Plot party trees in left-right orientation instead of the classical top-down layout.
  Version: 0.1-0 | Last change: 2018-01-04 11:27:23+01 | Rev.: 1917
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get ATR 0.1-0 from CRAN
  R install command: install.packages("ATR", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


RainTyrol

Precipitation Observations and NWP Forecasts from GEFS

  Precipitation observations for the month of July in the years 1985-2012 for 95 stations in Tyrol, Austria, obtained from EHYD. Numerical weather prediction (NWP) forecasts from GEFS.
  Version: 0.1-0 | Last change: 2018-01-09 10:57:25+01 | Rev.: 1928
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current
  R install command: install.packages("RainTyrol", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


disttree

Trees and Forests for Distributional Regression

  Infrastructure for fitting distributional regression trees and forests based on gamlss.dist families.
  Version: 0.1-0 | Last change: 2018-02-16 16:47:14+01 | Rev.: 2025
  Download: linux(.tar.gz) | windows(.zip) | Build status: Building
  R install command: install.packages("disttree", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


Old Version: 0.1-0 | Last change: 2018-02-16 16:00:04
Old Version Download: linux(.tar.gz) | windows(.zip)

evtree

Evolutionary Learning of Globally Optimal Trees

  Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The evtree package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the partykit package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.
  Version: 1.0-6 | Last change: 2017-12-15 09:27:53+01 | Rev.: 1882
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get evtree 1.0-6 from CRAN
  R install command: install.packages("evtree", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


glmertree

Generalized Linear Mixed Model Trees

  Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit.
  Version: 0.1-2 | Last change: 2018-01-05 01:17:44+01 | Rev.: 1922
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get glmertree 0.1-1 from CRAN
  R install command: install.packages("glmertree", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


lagsarlmtree

Spatial Lag Model Trees

  Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag.
  Version: 1.0-0 | Last change: 2017-10-08 23:40:13+02 | Rev.: 1639
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get lagsarlmtree 1.0-0 from CRAN
  R install command: install.packages("lagsarlmtree", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


loret

Logistic Regression Trees for Multinomial and Ordinal Responses

  A collection of functions to extend the functionality of mob. In particular, they can be used to extend the node model functionality for mob() in package partykit. Convenience functions for mob objects. Some data files.
  Version: 0.0-1 | Last change: 2017-10-08 23:44:32+02 | Rev.: 1640
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current
  R install command: install.packages("loret", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


model4you

Stratified and Personalised Models Based on Model-Based Trees and Forests

  Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) ; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) .
  Version: 0.9-1 | Last change: 2018-02-02 14:24:14+01 | Rev.: 1980
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get model4you 0.9-1 from CRAN
  R install command: install.packages("model4you", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


palmtree

Partially Additive (Generalized) Linear Model Trees

  This is an implementation of model-based trees with global model parameters (PALM trees). The PALM tree algorithm is an extension to the MOB algorithm (implemented in the partykit package), where some parameters are fixed across all groups. Details about the method can be found in Seibold, Hothorn, Zeileis (2016) . The package offers coef(), logLik(), plot(), and predict() functions for PALM trees.
  Version: 0.9-0 | Last change: 2018-01-16 10:58:32+01 | Rev.: 1951
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get palmtree 0.9-0 from CRAN
  R install command: install.packages("palmtree", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


partykit

A Toolkit for Recursive Partytioning

  A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources (rpart, RWeka, PMML) yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the party package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) .
  Version: 1.2-1 | Last change: 2018-02-09 12:59:17+01 | Rev.: 2000
  Download: linux(.tar.gz) | windows(.zip) | Build status: Failed to build | Stable Release: Get partykit 1.2-0 from CRAN
  R install command: install.packages("partykit", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


Old Version: 1.2-0 | Last change: 2017-06-22 10:22:03
Old Version Download: linux(.tar.gz) | windows(.zip)

partykitR1

A Toolkit for Recursive Partytioning (R1 Series)

  A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources (rpart, RWeka, PMML) yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the party package are provided based on the new infrastructure.
  Version: 1.1-2 | Last change: 2017-08-01 14:12:45+02 | Rev.: 1519
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current
  R install command: install.packages("partykitR1", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)

 

Build status codes

0 - Current: the package is available for download. The corresponding package passed checks on the Linux and Windows platform without ERRORs.
1 - Scheduled for build: the package has been recognized by the build system and provided in the staging area.
2 - Building: the package has been sent to the build machines. It will be built and checked using the latest patched version of R. Note that it is included in a batch of several packages. Thus, this process will take some time to finish.
3 - Failed to build: the package failed to build or did not pass the checks on the Linux and/or Windows platform. It is not made available since it does not meet the policies.
4 - Conflicts: two or more packages of the same name exist. None of them will be built. Maintainers are asked to negotiate further actions.
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