Package: LogisticEnsembles 1.0.2.9000

LogisticEnsembles: Automatically Runs 18 Logistic Models-14 Individual Logistic Models and 4 Ensembles of Models

Automatically returns results from 18 logistic models including 14 individual logistic models and 4 logistic ensembles of models. The package also returns 25 plots, 5 tables, and a summary report. The package automatically builds all 18 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. The package includes medical data (the Pima Indians data set), and information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver Operating Characteristic (ROC) curves for each of the 18 models.

Authors:Russ Conte [aut, cre, cph]

LogisticEnsembles_1.0.2.9000.tar.gz
LogisticEnsembles_1.0.2.9000.zip(r-4.7-any)LogisticEnsembles_1.0.2.9000.zip(r-4.6-any)LogisticEnsembles_1.0.2.9000.zip(r-4.5-any)
LogisticEnsembles_1.0.2.9000.tgz(r-4.6-any)LogisticEnsembles_1.0.2.9000.tgz(r-4.5-any)
LogisticEnsembles_1.0.2.9000.tar.gz(r-4.7-any)LogisticEnsembles_1.0.2.9000.tar.gz(r-4.6-any)
LogisticEnsembles_1.0.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
LogisticEnsembles/json (API)

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

Bug tracker:https://github.com/infinitecuriosity/logisticensembles/issues

Datasets:
  • Cervical_cancer - Cervical_cancer-This data set predicts a patient's risk of cervical cancer based on behavior reports
  • Diabetes - Diabetes—A logistic data set, determining whether a woman tested positive for diabetes. 100 percent accurate results are possible using the logistic function in the Ensembles package.
  • German_Credit_Risk - German_Credit_Risk-This dataset classifies people described by a set of attributes as good or bad credit risks. #'
  • Lebron - Lebron—A logistic data set, with the result indicating whether or not Lebron scored on each shot in the data set.
  • SAHeart - SAHeart data

On CRAN:

Conda:

6.12 score 3 stars 11 scripts 378 downloads 1 exports 221 dependencies

Last updated from:c0e1845be7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK272
source / vignettesOK263
linux-release-x86_64OK278
macos-release-arm64OK209
macos-oldrel-arm64OK186
windows-develOK198
windows-releaseOK182
windows-oldrelOK186
wasm-releaseOK205

Exports:Logistic

Dependencies:abindadabagarmbackportsbase64encbigDbitbit64bitopsbootbrnnbroombslibC50cachemcarcarDatacaretclassclassIntclicliprclockcodacodetoolscoincolorspacecombinatcommonmarkConsRankcorrplotcowplotcpp11crayonCubistcurldata.tableDerivdiagramdialsDiceDesigndigestdoBydoParalleldplyre1071evaluatefarverfastmapfontawesomeforcatsforeachforecastFormulafracdifffsfurrrfuturefuture.applygamgbmgenericsggplot2ggplotifyglmnetglobalsgluegoftestgowergridExtragridGraphicsgtgtablegtoolshardhathavenhighrhmshtmltoolshtmlwidgetshttpuvinumipredisobanditeratorsjquerylibjsonlitejuicyjuicekernlabKernSmoothklaRknitrlabelinglabelledlaterlatticelavalibcoinlifecyclelistenvlitedownlme4lmtestlubridateMachineShopmagrittrmarkdownMASSMatrixMatrixModelsmatrixStatsmdamemoisemgcvmimeminiUIminqaModelMetricsmodelrmodeltoolsmultcompmvtnormnlmenloptrnnetnortestnumDerivolsrrotelparallellypartypartykitpbkrtestpillarpkgconfigplsplyrpolsplineprettyunitspROCprodlimprogressprogressrpromisesproxypurrrquantregquestionrR.cacheR.methodsS3R.ooR.utilsR6randomForestrangerrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreactablereactRreadrrecipesreformulasreshape2rlangrlistrmarkdownrpartrprojrootrsampleRsolnprstudioapiS7sandwichsassscalessfdshapeshinyslidersourcetoolsSparseMsparsevctrsSQUAREMstringistringrstrucchangestylersurvivalTH.datatibbletidyrtidyselecttimechangetimeDatetinytextruncnormtzdburcautf8V8vctrsvipviridisLitevroomwarpwithrxfunxgboostXMLxml2xplorerrxtableyamlyardstickyulab.utilszoo

vignette_0_The_Hello_World_of_LogisticEnsembles
Installation | Example | 24 summary tables (three are shown): | Summary Report | Finding the strongest predictor from the most accurate model(s) | Grand Summary

Last update: 2026-02-26
Started: 2025-10-05

Logistic report template
Introduction | Statement of the problem from the customer's perspective | History of the problem, previous results | Exploratory data analysis | 24 logistic models (Individual models then ensembles, in alphabetical order) | Ensembles and individual model plots | Strongest evidence based results: | Five strongest evidence based recommendations | Conclusions | References

Last update: 2025-10-12
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vignette_1_the_data-colnum-numresamples

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vignette_2_remove_VIF_greater_than

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vignette_3_remove_ensemble_correlations_greater_than

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vignette_4_save_all_trained_models_and_save_all_plots

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vignette_5_how_to_handle_strings

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vignette_6_set_seed

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vignette_7_do_you_have_new_data

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vignette_8_Capstone-Using_multiple_features

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