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).
|
ATR | Alternative Tree Representation
|
| |
Plot party trees in left-right orientation instead of the
classical top-down layout. |
| |
Version: 0.1-1 |
Last change: 2020-01-09 15:26:44+01 |
Rev.: 2949 |
| |
Download:
 (.tar.gz) |
 (.zip) |
Build status: Current | Stable Release: Get ATR 0.1-1 from CRAN |
| |
R install command:
install.packages("ATR", repos="http://R-Forge.R-project.org") |
| |
|
|
circtree | Regression Trees and Forests for Circular Responses
|
| |
Infrastructure for fitting distributional trees and forests based on maximum-likelihood estimation of parameters for a circular response, as well as regression methods for a circular response based on maximum-likelihood estimation are provided. For both approaches the von Mises distribution is employed as circular response distribution. |
| |
Version: 0.1-0 |
Last change: 2020-12-01 09:53:24+01 |
Rev.: 3132 |
| |
Download:
(.tar.gz) |
(.zip) |
Build status: Current |
| |
R install command:
install.packages("circtree", repos="http://R-Forge.R-project.org") |
| |
|
|
disttree | Trees and Forests for Distributional Regression
|
| |
Infrastructure for fitting distributional regression trees and
forests based on maximum-likelihood estimation of parameters for specified
distribution families, for example from the GAMLSS family. |
| |
Version: 0.2-0 |
Last change: 2021-03-15 10:35:37+01 |
Rev.: 3189 |
| |
Download:
(.tar.gz) |
(.zip) |
Build status: Current |
| |
R install command:
install.packages("disttree", repos="http://R-Forge.R-project.org") |
| |
|
|
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: 1.0-8 |
Last change: 2023-01-27 02:49:18+01 |
Rev.: 3247 |
| |
Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get evtree 1.0-8 from CRAN |
| |
R install command:
install.packages("evtree", repos="http://R-Forge.R-project.org") |
| |
|
|
lagsarlmtree | Spatial Lag Model Trees
|
| |
Model-based linear model trees adjusting for spatial correlation using a
simultaneous autoregressive spatial lag, Wagner and Zeileis (2019)
. |
| |
Version: 1.0-1 |
Last change: 2021-01-08 14:47:40+01 |
Rev.: 3156 |
| |
Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get lagsarlmtree 1.0-1 from CRAN |
| |
R install command:
install.packages("lagsarlmtree", repos="http://R-Forge.R-project.org") |
| |
|
|
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:
(.tar.gz) |
(.zip) |
Build status: Current |
| |
R install command:
install.packages("loret", repos="http://R-Forge.R-project.org") |
| |
|
|
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: 0.9-7 |
Last change: 2021-11-25 15:28:17+01 |
Rev.: 3225 |
| |
Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get model4you 0.9-7 from CRAN |
| |
R install command:
install.packages("model4you", repos="http://R-Forge.R-project.org") |
| |
|
|
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 (2019) . The package offers coef(),
logLik(), plot(), and predict() functions for PALM trees. |
| |
Version: 1.0-0 |
Last change: 2021-10-09 04:00:55+02 |
Rev.: 3224 |
| |
Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get palmtree 0.9-1 from CRAN |
| |
R install command:
install.packages("palmtree", repos="http://R-Forge.R-project.org") |
| |
|
|
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: 1.2-22 |
Last change: 2024-08-17 14:33:09+02 |
Rev.: 3291 |
| |
Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get partykit 1.2-22 from CRAN |
| |
R install command:
install.packages("partykit", repos="http://R-Forge.R-project.org") |
| |
|
|
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:
(.tar.gz) |
(.zip) |
Build status: Current |
| |
R install command:
install.packages("partykitR1", repos="http://R-Forge.R-project.org") |
| |
|
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.
5 - Offline: the package is not available. The build system may be offline or the package maintainer did not trigger a rebuild (done e.g., via committing to the package repository).
If your package is not shown on this page or not building, then check the build system status report.
