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:
party_1.3-17.tar.gz
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party.pdf |party.html✨
party/json (API)
NEWS
| # Install 'party' in R: |
| install.packages('party', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://r-forge.r-project.org/projects/party
- readingSkills - Reading Skills
Last updated 22 days agofrom:a81e5a70ef. Checks:OK: 8 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 17 2024 |
| R-4.5-win-x86_64 | OK | Aug 17 2024 |
| R-4.5-linux-x86_64 | NOTE | Aug 17 2024 |
| R-4.4-win-x86_64 | OK | Aug 17 2024 |
| R-4.4-mac-x86_64 | OK | Aug 17 2024 |
| R-4.4-mac-aarch64 | OK | Aug 17 2024 |
| R-4.3-win-x86_64 | OK | Aug 17 2024 |
| R-4.3-mac-x86_64 | OK | Aug 17 2024 |
| R-4.3-mac-aarch64 | OK | Aug 17 2024 |
Exports:cforestcforest_classicalcforest_controlcforest_unbiasedconditionalTreectreectree_controledge_simplefitinitializeinitVariableFramemobmob_controlnode_barplotnode_bivplotnode_boxplotnode_densitynode_histnode_innernode_scatterplotnode_survnode_terminalnodesparty_internprettytreeproximityptraforesponsereweightsctest.mobtreeresponsevarimpvarimpAUCwhere
Dependencies:codetoolscoinlatticelibcoinMASSMatrixmatrixStatsmodeltoolsmultcompmvtnormsandwichstrucchangesurvivalTH.datazoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| 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 Forest | cforest proximity |
| Conditional Inference Trees | conditionalTree ctree |
| Control for Conditional Inference Trees | ctree_control |
| Control for Conditional Tree Forests | cforest_classical cforest_control cforest_unbiased |
| Fit `StatModel' Objects to Data | fit,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 objects | initVariableFrame initVariableFrame,data.frame-method initVariableFrame,matrix-method initVariableFrame-methods |
| Class "LearningSample" | LearningSample-class |
| Model-based Recursive Partitioning | coef.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 Partitioning | mob_control |
| Panel-Generators for Visualization of Party Trees | edge_simple node_barplot node_bivplot node_boxplot node_density node_hist node_inner node_scatterplot node_surv node_terminal |
| Visualization of Binary Regression Trees | plot.BinaryTree |
| Visualization of MOB Trees | plot.mob |
| Print a tree. | prettytree |
| Class "RandomForest" | RandomForest-class show,RandomForest-method treeresponse,RandomForest-method weights,RandomForest-method where,RandomForest-method |
| Reading Skills | readingSkills |
| Re-fitting Models with New Weights | reweight reweight.glinearModel reweight.linearModel |
| Class "SplittingNode" | SplittingNode-class TerminalModelNode-class TerminalNode-class |
| Function for Data Transformations | ff_trafo ptrafo |
| Class "TreeControl" | TreeControl TreeControl-class |
| Variable Importance | varimp varimpAUC |
