Package: qeML 1.2.1

qeML: Quick and Easy Machine Learning Tools

The letters 'qe' in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface.

Authors:Norm Matloff [aut, cre]

qeML_1.2.1.tar.gz
qeML_1.2.1.zip(r-4.5)qeML_1.2.1.zip(r-4.4)qeML_1.2.1.zip(r-4.3)
qeML_1.2.1.tgz(r-4.4-any)qeML_1.2.1.tgz(r-4.3-any)
qeML_1.2.1.tar.gz(r-4.5-noble)qeML_1.2.1.tar.gz(r-4.4-noble)
qeML_1.2.1.tgz(r-4.4-emscripten)qeML_1.2.1.tgz(r-4.3-emscripten)
qeML.pdf |qeML.html
qeML/json (API)

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

Peer review:

Bug tracker:https://github.com/matloff/qeml/issues

Datasets:

On CRAN:

8.27 score 40 stars 1 packages 23 scripts 277 downloads 56 exports 125 dependencies

Last updated 8 days agofrom:0116a8452c. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-winERRORNov 14 2024
R-4.5-linuxERRORNov 14 2024
R-4.4-winERRORNov 14 2024
R-4.4-macERRORNov 14 2024
R-4.3-winERRORNov 14 2024
R-4.3-macERRORNov 14 2024

Exports:buildQEcallcartesianFactorcheckPkgLoadedDatadataToTopLevelsdoubleDevalrfactorToTopLevelslevelCountsnewDFRowplotClassesUMAPplotPairedResidsqeAdaBoostqeCompareqeDeepnetqeDTqeFOCIqeFOCImultqeFOCIrandqeFreqParcoordqeFTqeGBoostqeKNNqeKNNMVqeLASSOqeLeaveOut1VarqeLightGBoostqeLinqeLinKNNqeLinMVqeLogitqeLogitMVqeMittalGraphqeNCVregCVqeNeuralqeNeuralTorchqeParallelqePCAqePlotCurvesqePolyLASSOqePolyLinqePolyLinKNNqePolyLogqeRareLevelsqeRFqeRFgrfqeRFrangerqeROCqeRpartqeSVMqeTextqeTSqeUMAPqeXGBoostreplicMeansMatrixwideToLongWithTime

Dependencies:abindbackportsbase64encBHbootbroombslibcachemcarcarDataclicodetoolscolorspacecowplotcpp11data.tableDerivDiceKrigingdigestdoBydplyrevaluatefansifarverfastmapfloatFNNFOCIfontawesomeforeachFormulafsgbmgenericsggplot2glmnetgluegmpgrfgtablegtoolshighrhtmltoolsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelgrlifecyclelme4lmtestmagrittrMASSMatrixMatrixExtraMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamlapimodelrmunsellmvtnormnlmenloptrNLPnnetnumDerivpartoolspbkrtestpdistpillarpkgconfigpolyregproxypurrrquantregR.methodsS3R.ooR.utilsR6RANNrappdirsRColorBrewerRcppRcppArmadilloRcppEigenregtoolsRhpcBLASctlrjerlangrmarkdownrpartrpart.plotrsparsesandwichsassscalesshapeslamSparseMstringistringrsurvivaltext2vectibbletidyrtidyselecttinytextmtoweranNAtufteutf8vctrsviridisLitewithrxfunxml2yamlzoo

Feature_Selection

Rendered fromFeature_Selection.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-19
Started: 2023-02-22

Function List

Rendered fromFunction_List.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-19
Started: 2023-08-16

Machine Learning Overview

Rendered fromML_Overview.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-20
Started: 2023-02-22

Problems with P-values

Rendered fromNoPValues.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-27
Started: 2023-09-02

Overfitting

Rendered fromOverfitting.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-19
Started: 2023-02-22

PCA and UMAP

Rendered fromPCA_and_UMAP.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-19
Started: 2023-02-22

Quick Start

Rendered fromQuick_Start.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-19
Started: 2023-02-22

Unbalanced Classes

Rendered fromUnbalanced_Classes.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2023-09-20
Started: 2023-02-22

Readme and manuals

Help Manual

Help pageTopics
Advanced PlotsplotClassesUMAP plotPairedResids qeFreqParcoord qeMittalGraph qePlotCurves
Swedish breast cancer.CancerMenopause
Records from several offerings of a certain course.courseRecords
Pre-Euro Era Currency Fluctuationscurrency
Bike sharing data.day day1 day2
Double Descent PhenomenondoubleD plot.doubleD
Employee Attrition DataempAttrition
English vocabulary dataenglish
EPI Growth DataEPIWgProduct
Feature Selection and Model Buildingpredict.qeText predict.qeTS qeCompare qeFT qeText qeTS
Subset of the Covertype data.forest500
Iranian Customer Churn DatairanChurn
Law School Admissions Datalsa
Letter Frequenciesltrfreqs
Major Leage Baseball player data set.mlb mlb1
MovieLens User Summary Datamlens
UCI adult income data set, adaptednewAdult newadult
New York City Taxi Datanyctaxi
Italian olive oils data set.oliveoils
Prediction with Missing Valuespredict.qeKNNMV predict.qeLinMV predict.qeLogitMV qeKNNMV qeLinMV qeLogitMV
Silicon Valley programmers and engineers datapef prgeng svcensus
Quick-and-Easy Machine Learning WrapperscheckPkgLoaded plot.qeLASSO plot.qePoly plot.qeRF plot.qeRpart predict.qeAdaBoost predict.qeDeepnet predict.qeGBoost predict.qeIso predict.qeKNN predict.qeLASSO predict.qeLightGBoost predict.qeLin predict.qeLogit predict.qeNCVregCV predict.qeNeural predict.qeParallel predict.qePCA predict.qePoly predict.qePolyLin predict.qePolyLinKNN predict.qePolyLog predict.qeRF predict.qeRFgrf predict.qeRFranger predict.qeRpart predict.qeSVM predict.qeUMAP qeAdaBoost qeDeepnet qeDT qeFOCI qeFOCImult qeFOCIrand qeGBoost qeIso qeKNN qeLASSO qeLightGBoost qeLin qeLinKNN qeLogit qeNCVregCV qeNeural qeParallel qePCA qePoly qePolyLASSO qePolyLin qePolyLinKNN qePolyLog qeRF qeRFgrf qeRFranger qeROC qeRpart qeSVM qeUMAP qeXGBoost
Course quiz documentsquizDocs quizzes
R Factor UtilitiescartesianFactor dataToTopLevels factorToTopLevels levelCounts qeRareLevels
Thyroid DiseaseThyroidDisease
UtilitiesbuildQEcall evalr newDFRow
UtilitiesbuildQEcall Data evalr newDFRow replicMeansMatrix wideToLongWithTime
Variable Importance MeasuresqeLeaveOut1Var
Weather Time SeriesweatherTS