Package: party 1.3-17

Torsten Hothorn

party: A Laboratory for Recursive Partytioning

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>.

Authors:Torsten Hothorn [aut, cre], Kurt Hornik [aut], Carolin Strobl [aut], Achim Zeileis [aut]

party_1.3-17.tar.gz
party_1.3-17.tar.gz(r-4.5-noble)party_1.3-17.tar.gz(r-4.4-noble)
party_1.3-17.tgz(r-4.4-emscripten)party_1.3-17.tgz(r-4.3-emscripten)
party.pdf |party.html
party/json (API)
NEWS

# Install 'party' in R:
install.packages('party', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://r-forge.r-project.org/projects/party

Uses libs:
  • openblas– Optimized BLAS
Datasets:

10.30 score 4 stars 30 packages 2.8k scripts 25k downloads 76 mentions 34 exports 15 dependencies

Last updated 4 months agofrom:780aa4f5d2. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-linux-x86_64OKOct 31 2024

Exports:cforestcforest_classicalcforest_controlcforest_unbiasedconditionalTreectreectree_controledge_simplefitinitializeinitVariableFramemobmob_controlnode_barplotnode_bivplotnode_boxplotnode_densitynode_histnode_innernode_scatterplotnode_survnode_terminalnodesparty_internprettytreeproximityptraforesponsereweightsctest.mobtreeresponsevarimpvarimpAUCwhere

Dependencies:codetoolscoinlatticelibcoinMASSMatrixmatrixStatsmodeltoolsmultcompmvtnormsandwichstrucchangesurvivalTH.datazoo

party with the mob

Rendered fromMOB.Rnwusingutils::Sweaveon Oct 31 2024.

Last update: 2020-03-05
Started: 2012-02-17

party: A Laboratory for Recursive Partytioning

Rendered fromparty.Rnwusingutils::Sweaveon Oct 31 2024.

Last update: 2021-02-08
Started: 2012-02-17

Readme and manuals

Help Manual

Help pageTopics
Class "BinaryTree"BinaryTree-class nodes nodes,BinaryTree,integer-method nodes,BinaryTree,numeric-method nodes-methods response response,BinaryTree-method response-methods show,BinaryTree-method treeresponse treeresponse,BinaryTree-method treeresponse-methods weights weights,BinaryTree-method weights-methods where where,BinaryTree-method where-methods
Random Forestcforest proximity
Conditional Inference TreesconditionalTree ctree
Control for Conditional Inference Treesctree_control
Control for Conditional Tree Forestscforest_classical cforest_control cforest_unbiased
Fit `StatModel' Objects to Datafit,StatModel,LearningSample-method fit-methods
Class "ForestControl"ForestControl-class
Methods for Function initialize in Package `party'initialize initialize,ExpectCovar-method initialize,ExpectCovarInfluence-method initialize,LinStatExpectCovar-method initialize,LinStatExpectCovarMPinv-method initialize,svd_mem-method initialize,VariableFrame-method initialize-methods
Set-up VariableFrame objectsinitVariableFrame initVariableFrame,data.frame-method initVariableFrame,matrix-method initVariableFrame-methods
Class "LearningSample"LearningSample-class
Model-based Recursive Partitioningcoef.mob deviance.mob fitted.mob logLik.mob mob mob-class predict.mob print.mob residuals.mob sctest.mob summary.mob weights.mob
Control Parameters for Model-based Partitioningmob_control
Panel-Generators for Visualization of Party Treesedge_simple node_barplot node_bivplot node_boxplot node_density node_hist node_inner node_scatterplot node_surv node_terminal
Visualization of Binary Regression Treesplot.BinaryTree
Visualization of MOB Treesplot.mob
Print a tree.prettytree
Class "RandomForest"RandomForest-class show,RandomForest-method treeresponse,RandomForest-method weights,RandomForest-method where,RandomForest-method
Reading SkillsreadingSkills
Re-fitting Models with New Weightsreweight reweight.glinearModel reweight.linearModel
Class "SplittingNode"SplittingNode-class TerminalModelNode-class TerminalNode-class
Function for Data Transformationsff_trafo ptrafo
Class "TreeControl"TreeControl TreeControl-class
Variable Importancevarimp varimpAUC