Package: ModTools 0.9.15

ModTools: Building Regression and Classification Models

Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented 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/json (API)
NEWS

# Install 'ModTools' in R:

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

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

Pkgdown site:https://andrisignorell.github.io

Datasets:

d.glass - Measurements of Forensic Glass Fragments
d.pima - Diabetes survey on Pima Indians
d.pima2 - Diabetes survey on Pima Indians

On CRAN:

4.20 score 2 stars 3 scripts 746 downloads 2 mentions 21 exports 123 dependencies

Last updated 3 months agofrom:5a29e79544. Checks:9 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 15 2025
R-4.5-win	OK	Mar 15 2025
R-4.5-mac	OK	Mar 15 2025
R-4.5-linux	OK	Mar 15 2025
R-4.4-win	OK	Mar 15 2025
R-4.4-mac	OK	Mar 15 2025
R-4.4-linux	OK	Mar 15 2025
R-4.3-win	OK	Mar 15 2025
R-4.3-mac	OK	Mar 15 2025

Exports:BestCut BreuschPaganTest CoeffDiffCI CP FitMod LeafRates LogitBoost Node OverSample PlotLift PredictCI RefLevel Response RobSummary ROC Rules SplitTrainTest TModC Tune UnderSample VarImp

Dependencies:abind AER askpass backports bit bit64 boot broom C50 car carData cellranger class cli clipr colorspace corpcor cowplot cpp11 crayon Cubist curl data.table DBI DEoptimR Deriv DescTools doBy dplyr e1071 Exact expm fansi farver forcats Formula generics ggplot2 gld glue gtable haven hms httr inum isoband jsonlite labeling lattice libcoin lifecycle lme4 lmom lmtest magrittr MASS Matrix MatrixModels mgcv microbenchmark mime minqa mitools modelr munsell mvtnorm naivebayes NeuralNetTools nlme nloptr nnet numDeriv openssl partykit pbkrtest pillar pkgconfig plyr prettyunits pROC progress proxy pscl purrr quantreg R6 randomForest rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr readxl reformulas relaimpo rematch reshape2 rlang robustbase rootSolve rpart rpart.plot rstudioapi sandwich scales SparseM stringi stringr survey survival sys tibble tidyr tidyselect tzdb utf8 vctrs viridisLite vroom withr zoo

Help page	Topics
Regression and Classification Tools	ModTools-package ModTools
Best Cutpoint for a ROC Curve	BestCut
Breusch-Pagan Test	BreuschPaganTest
Confidence Interval for the Difference of Two Coefficients in a Linear Model	CoeffDiffCI
Complexity Parameter of an rpart Model	CP plot.CP print.CP
Measurements of Forensic Glass Fragments	d.glass
Diabetes survey on Pima Indians	d.pima d.pima2
Wrapper for Several Model Functions	drop1.FitMod FitMod plot.FitMod predict.FitMod print.FitMod summary.FitMod
Leafrates for the Nodes of an 'rpart' Tree	LeafRates plot.LeafRates Purity
LogitBoost Classification Algorithm	LogitBoost LogitBoost.default LogitBoost.formula
Nodes and Splits in an rpart Tree	Node Splits
Oversample and Undersample	OverSample UnderSample
Lift Charts to Compare Binary Predictive Models	PlotLift
Confidence Intervals for Predictions of a GLM	PredictCI
Used Reference Levels in a Linear Model	RefLevel
Extract the Response from Several Models	Response
Robust Summary for Linear Models	RobSummary
Build a ROC curve	ROC
Extract Rules from 'rpart' Object	Rules
Split DataFrame in Train an Test Sample	SplitTrainTest
Compare Classification Models	plot.TModC TModC
Tobit Regression	Tobit
Tune Classificators	Tune
Variable Importance for Regression and Classification Models	GarsonWeights plot.VarImp print.VarImp VarImp VarImp.default VarImp.FitMod

Package: ModTools 0.9.15

ModTools: Building Regression and Classification Models

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