Package: ModTools 0.9.12.1

ModTools: Tools for Building Regression and Classification Models

Collection of tools for regression and classification tasks. The package implements a consistent user interface to the most popular regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, and complements it with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.

Authors:Andri Signorell [aut, cre], Bernhard Compton [ctb], Marcel Dettling [ctb], Alexandre Hainard [ctb], Max Kuhn [ctb], Frédérique Lisacek [ctb], Michal Majka [ctb], Markus Müller [ctb], Dan Putler [ctb], Jean-Charles Sanchez [ctb], Natalia Tiberti [ctb], Natacha Turck [ctb], Jarek Tuszynski [ctb], Robin Xavier [ctb], Achim Zeileis [ctb]

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ModTools.pdf |ModTools.html✨
ModTools/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/andrisignorell/modtools/issues

Datasets:
  • bioChemists - Article production by graduate students in biochemistry Ph.D. programs
  • d.glass - Measurements of Forensic Glass Fragments
  • d.pima - Diabetes survey on Pima Indians
  • d.pima2 - Diabetes survey on Pima Indians

On CRAN:

22 exports 2 stars 1.87 score 111 dependencies 2 mentions 3 scripts 1.3k downloads

Last updated 1 months agofrom:5f5c485ae9. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 09 2024
R-4.5-winERRORSep 09 2024
R-4.5-linuxERRORSep 09 2024
R-4.4-winERRORSep 09 2024
R-4.4-macERRORSep 09 2024
R-4.3-winERRORSep 09 2024
R-4.3-macERRORSep 09 2024

Exports:BestCutBreuschPaganTestCoeffDiffCICPFitModLeafRatesLogitBoostNodeOverSamplePlotLiftPredictCIRefLevelResponseRobSummaryROCRulesSplitTrainTestTModCTuneUnderSampleVarImpzeroinfl

Dependencies:abindAERaskpassbackportsbootbroomC50carcarDatacellrangerclassclicolorspacecorpcorcowplotcpp11crayonCubistcurldata.tableDBIDEoptimRDerivDescToolsdoBydplyre1071ExactexpmfansifarverFormulagenericsggplot2gldgluegtablehmshttrinumisobandjsonlitelabelinglatticelibcoinlifecyclelme4lmomlmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkmimeminqamitoolsmodelrmunsellmvtnormnaivebayesNeuralNetToolsnlmenloptrnnetnumDerivopensslpartykitpbkrtestpillarpkgconfigplyrprettyunitspROCprogressproxypurrrquantregR6randomForestRColorBrewerRcppRcppArmadilloRcppEigenreadxlrelaimporematchreshape2rlangrobustbaserootSolverpartrpart.plotrstudioapisandwichscalesSparseMstringistringrsurveysurvivalsystibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Regression and Classification ToolsModTools-package ModTools
Best Cutpoint for a ROC CurveBestCut
article production by graduate students in biochemistry Ph.D. programsbioChemists
Breusch-Pagan TestBreuschPaganTest
Confidence Interval for the Difference of Two Coefficients in a Linear ModelCoeffDiffCI
Complexity Parameter of an rpart ModelCP plot.CP print.CP
Measurements of Forensic Glass Fragmentsd.glass
Diabetes survey on Pima Indiansd.pima d.pima2
Wrapper for Several Model Functionsdrop1.FitMod FitMod plot.FitMod predict.FitMod print.FitMod summary.FitMod
Leafrates for the Nodes of an 'rpart' TreeLeafRates plot.LeafRates Purity
LogitBoost Classification AlgorithmLogitBoost LogitBoost.default LogitBoost.formula
Nodes and Splits in an rpart TreeNode Splits
Oversample and UndersampleOverSample UnderSample
Lift Charts to Compare Binary Predictive ModelsPlotLift
Methods for zeroinfl Objectscoef.zeroinfl extractAIC.zeroinfl fitted.zeroinfl logLik.zeroinfl model.matrix.zeroinfl predict.zeroinfl predprob.zeroinfl print.summary.zeroinfl residuals.zeroinfl summary.zeroinfl terms.zeroinfl vcov.zeroinfl
Confidence Intervals for Predictions of a GLMPredictCI
Used Reference Levels in a Linear ModelRefLevel
Extract the Response from Several ModelsResponse
Robust Summary for Linear ModelsRobSummary
Build a ROC curveROC
Extract Rules from 'rpart' ObjectRules
Split DataFrame in Train an Test SampleSplitTrainTest
Compare Classification Modelsplot.TModC TModC
Tobit RegressionTobit
Tune ClassificatorsTune
Variable Importance for Regression and Classification ModelsGarsonWeights plot.VarImp print.VarImp VarImp VarImp.default VarImp.FitMod
Zero-inflated Count Data Regressionprint.zeroinfl zeroinfl
Control Parameters for Zero-inflated Count Data Regressionzeroinfl.control