Package: fdm2id 0.9.9

fdm2id: Data Mining and R Programming for Beginners

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Authors:Alexandre Blansché [aut, cre]

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fdm2id.pdf |fdm2id.html
fdm2id/json (API)

# Install 'fdm2id' in R:
install.packages('fdm2id', repos = c('https://blansche.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.53 score 1 stars 34 scripts 455 downloads 114 exports 136 dependencies

Last updated 1 years agofrom:c55e577541. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-winOKNov 03 2024
R-4.5-linuxOKNov 03 2024
R-4.4-winNOTENov 03 2024
R-4.4-macNOTENov 03 2024
R-4.3-winNOTENov 03 2024
R-4.3-macNOTENov 03 2024

Exports:ADABOOSTAPRIORIaugmentationBAGGINGboxclusCACARTcartdepthcartinfocartleafscartnodescartplotCDAclosegraphicscomparecompare.accuracycompare.jaccardcompare.kappaconfusioncookplotcorrelatedcost.curvesdata.diagdata.gaussdata.paraboldata.target1data.target2data.twomoonsdata.xorDBSCANdistplotEMevaluationevaluation.accuracyevaluation.adjr2evaluation.fmeasureevaluation.fowlkesmallowsevaluation.goodnessevaluation.jaccardevaluation.kappaevaluation.msepevaluation.precisionevaluation.r2evaluation.recallexportgraphicsexportgraphics.offexportgraphics.onFEATURESELECTIONfilter.rulesfrequentwordsgeneral.rulesgetvocabGRADIENTBOOSTINGHCAinternintern.dunnintern.interclassintern.intraclasskaiserKERREGKMEANSkmeans.getkKNNLDAleverageplotLINREGloadtextLRMCAMEANSHIFTMLPMLPREGNBNMFPCAperformanceplotavspplotcloudplotclusplotdataplotzipfPOLYREGpseudoFQDAquery.docsquery.wordsRANDOMFORESTregplotresplotroc.curvesrotationrunningtimescatterplotselectfeaturesSOMSPECTRALsplitdatastabilitySTUMPSVDSVMSVMlSVMrSVRSVRlSVRrTEXTMININGtoggleexporttoggleexport.offtoggleexport.ontreeplotTSNEvectorize.docsvectorize.words

Dependencies:abindarulesarulesVizaskpassbackportsbase64encbootbroombslibcacachemcarcarDatacliclustercodetoolscolorspacecowplotcpp11crosstalkcurldata.tableDerivdigestdoBydplyrDTellipseemmeansestimabilityevaluateFactoMineRfansifarverfastmapflashClustfontawesomeforeachFormulafsgclusgenericsggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtablehighrhtmltoolshtmlwidgetshttpuvhttrigraphisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmclustmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpermutepillarpkgconfigplotlyplspolyclippromisespurrrqapquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenregistryrlangrmarkdownsassscalesscatterplot3dseriationSparseMstringistringrsurvivalsyssystemfontstibbletidygraphtidyrtidyselecttinytexTSPtweenrutf8vcdvctrsveganviridisviridisLitevisNetworkwithrxfunyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Sample of car accident location in the UK during year 2014.accident2014
Classification using AdaBoostADABOOST
Alcohol datasetalcohol
Classification using APRIORIAPRIORI
APRIORI classification modelapriori-class
Duplicate and add noise to a datasetaugmentation
Auto MPG datasetautompg
Classification using BaggingBAGGING
Flea beetles datasetbeetles
Birth datasetbirth
Boosting methods modelboosting-class
Clustering Box Plotsboxclus
Population and location of 18 major british cities.britpop
Correspondence Analysis (CA)CA
Classification using CARTCART
Depthcartdepth
CART informationcartinfo
Number of Leafscartleafs
Number of Nodescartnodes
CART Plotcartplot
Classification using Canonical Discriminant AnalysisCDA
Canonical Disciminant Analysis modelcda-class
Close a graphics deviceclosegraphics
Comparison of two sets of clusterscompare
Comparison of two sets of clusters, using accuracycompare.accuracy
Comparison of two sets of clusters, using Jaccard indexcompare.jaccard
Comparison of two sets of clusters, using kappacompare.kappa
Confuion matrixconfusion
Cookies datasetcookies cookies.desc.test cookies.desc.train cookies.y.test cookies.y.train
Plot the Cook's distance of a linear regression modelcookplot
Correlated variablescorrelated
Plot Cost Curvescost.curves
Credit datasetcredit
Square datasetdata.diag
Gaussian mixture datasetdata.gauss
Parabol datasetdata.parabol
Target1 datasetdata.target1
Target2 datasetdata.target2
Two moons datasetdata.twomoons
XOR datasetdata.xor
"data1" datasetdata1
"data2" datasetdata2
"data3" datasetdata3
Training set and test setdataset-class
DBSCAN modeldbs-class
DBSCAN clustering methodDBSCAN
Decathlon datasetdecathlon
Plot a k-distance graphicdistplot
Expectation-Maximization clustering methodEM
Expectation-Maximization modelem-class
Eucalyptus dataseteucalyptus
Evaluation of classification or regression predictionsevaluation
Accuracy of classification predictionsevaluation.accuracy
Adjusted R2 evaluation of regression predictionsevaluation.adjr2
F-measureevaluation.fmeasure
Fowlkes–Mallows indexevaluation.fowlkesmallows
Goodnessevaluation.goodness
Jaccard indexevaluation.jaccard
Kappa evaluation of classification predictionsevaluation.kappa
MSEP evaluation of regression predictionsevaluation.msep
Precision of classification predictionsevaluation.precision
R2 evaluation of regression predictionsevaluation.r2
Recall of classification predictionsevaluation.recall
Open a graphics deviceexportgraphics
Toggle graphic exportsexportgraphics.off exportgraphics.on toggleexport toggleexport.off toggleexport.on
Factorial analysis resultsfactorial-class
Classification with Feature selectionFEATURESELECTION
Filtering a set of rulesfilter.rules
Frequent wordsfrequentwords
Remove redundancy in a set of rulesgeneral.rules
Extract words and phrases from a corpusgetvocab
Classification using Gradient BoostingGRADIENTBOOSTING
Hierarchical Cluster Analysis methodHCA
Clustering evaluation through internal criteriaintern
Clustering evaluation through Dunn's indexintern.dunn
Clustering evaluation through interclass inertiaintern.interclass
Clustering evaluation through intraclass inertiaintern.intraclass
Ionosphere datasetionosphere
Kaiser rulekaiser
Kernel RegressionKERREG
K-means methodKMEANS
Estimation of the number of clusters for _K_-meanskmeans.getk
Classification using k-NNKNN
K Nearest Neighbours modelknn-class
Classification using Linear Discriminant AnalysisLDA
Plot the leverage points of a linear regression modelleverageplot
Linear RegressionLINREG
Linsep datasetlinsep
load a text fileloadtext
Classification using Logistic RegressionLR
Multiple Correspondence Analysis (MCA)MCA
MeanShift methodMEANSHIFT
MeanShift modelmeanshift-class
Classification using Multilayer PerceptronMLP
Multi-Layer Perceptron RegressionMLPREG
Generic classification or regression modelmodel-class
Movies datasetmovies
Classification using Naive BayesNB
Non-negative Matrix FactorizationNMF
Ozone datasetozone
Learning Parametersparams-class
Principal Component Analysis (PCA)PCA
Performance estimationperformance
Plot function for cda-classplot.cda
Plot function for factorial-classplot.factorial
Plot function for som-classplot.som
Plot actual vs. predictionsplotavsp
Plot word cloudplotcloud
Generic Plot Method for Clusteringplotclus
Advanced plot functionplotdata
Plot rank versus frequencyplotzipf
Polynomial RegressionPOLYREG
Model predictionspredict.apriori
Model predictionspredict.boosting
Model predictionspredict.cda
Predict function for DBSCANpredict.dbs
Predict function for EMpredict.em
Predict function for K-meanspredict.kmeans
Model predictionspredict.knn
Predict function for MeanShiftpredict.meanshift
Model predictionspredict.model
Model predictionspredict.selection
Model predictionspredict.textmining
Print a classification model obtained by APRIORIprint.apriori
Plot function for factorial-classprint.factorial
Pseudo-FpseudoF
Classification using Quadratic Discriminant AnalysisQDA
Document queryquery.docs
Word queryquery.words
Classification using Random ForestRANDOMFOREST
reg1 datasetreg1 reg1.test reg1.train
reg2 datasetreg2 reg2.test reg2.train
Plot function for a regression modelregplot
Plot the studentized residuals of a linear regression modelresplot
Plot ROC Curvesroc.curves
Rotationrotation
Running timerunningtime
Clustering Scatter Plotsscatterplot
Feature selection for classificationselectfeatures
Feature selectionselection-class
Snore datasetsnore
Self-Organizing Maps clustering methodSOM
Self-Organizing Maps modelsom-class
Spectral clustering methodSPECTRAL
Spectral clustering modelspectral-class
Spine datasetspine spine.test spine.train
Splits a dataset into training set and test setsplitdata
Clustering evaluation through stabilitystability
Classification using one-level decision treeSTUMP
Print summary of a classification model obtained by APRIORIsummary.apriori
Singular Value DecompositionSVD
Classification using Support Vector MachineSVM
Classification using Support Vector Machine with a linear kernelSVMl
Classification using Support Vector Machine with a radial kernelSVMr
Regression using Support Vector MachineSVR
Regression using Support Vector Machine with a linear kernelSVRl
Regression using Support Vector Machine with a radial kernelSVRr
Temperature datasettemperature
Text miningTEXTMINING
Text mining objecttextmining-class
Titanic datasettitanic
Dendrogram Plotstreeplot
t-distributed Stochastic Neighbor EmbeddingTSNE
University datasetuniversite
Document vectorizationvectorize.docs
Word vectorizationvectorize.words
Document vectorization objectvectorizer-class
Vowels datasetvowels vowels.test vowels.train
Wheat datasetwheat
Wine datasetwine
Zoo datasetzoo