Package: RMOA 1.1.0
Jan Wijffels
RMOA: Connect R with MOA for Massive Online Analysis
Connect R with MOA (Massive Online Analysis - <https://moa.cms.waikato.ac.nz/>) to build classification models and regression models on streaming data or out-of-RAM data. Also streaming recommendation models are made available.
Authors:
RMOA_1.1.0.tar.gz
RMOA_1.1.0.tar.gz(r-4.5-noble)RMOA_1.1.0.tar.gz(r-4.4-noble)
RMOA_1.1.0.tgz(r-4.4-emscripten)RMOA_1.1.0.tgz(r-4.3-emscripten)
RMOA.pdf |RMOA.html✨
RMOA/json (API)
NEWS
| # Install 'RMOA' in R: |
| install.packages('RMOA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jwijffels/rmoa/issues
Last updated 2 years agofrom:c92e804194. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 15 2024 |
| R-4.5-linux | OK | Dec 15 2024 |
Exports:AccuracyUpdatedEnsembleAccuracyWeightedEnsembleActiveClassifierADACCAdaHoeffdingOptionTreeAMRulesRegressorASHoeffdingTreeBaselinePredictorBRISMFPredictorDACCdatastreamdatastream_csvdatastream_csv2datastream_dataframedatastream_delimdatastream_delim2datastream_ffdfdatastream_filedatastream_matrixdatastream_tableDecisionStumpfactoriseFadingTargetMeanFIMTDDHoeffdingAdaptiveTreeHoeffdingOptionTreeHoeffdingTreeLeveragingBagLimAttClassifierLimAttHoeffdingTreeMOA_classifierMOA_recommenderMOA_regressorMOAattributesMOAoptionsNaiveBayesNaiveBayesMultinomialOCBoostOnlineAccuracyUpdatedEnsembleORTOOzaBagOzaBagAdwinOzaBagASHTOzaBoostOzaBoostAdwinPerceptronRandomHoeffdingTreeTargetMeanTemporallyAugmentedClassifiertrainMOAWeightedMajorityAlgorithm
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Datastream objects and methods | datastream |
| data streams on a data.frame | datastream_dataframe |
| data streams on an ffdf | datastream_ffdf |
| File data stream | datastream_csv datastream_csv2 datastream_delim datastream_delim2 datastream_file datastream_table |
| data streams on a matrix | datastream_matrix |
| Convert character strings to factors in a dataset | factorise |
| MOA active learning classification | ActiveClassifier MOA_classification_activelearning |
| MOA bayesian classification | MOA_classification_bayes NaiveBayes NaiveBayesMultinomial |
| MOA classification using ensembles | AccuracyUpdatedEnsemble AccuracyWeightedEnsemble ADACC DACC LeveragingBag LimAttClassifier MOA_classification_ensemblelearning OCBoost OnlineAccuracyUpdatedEnsemble OzaBag OzaBagAdwin OzaBagASHT OzaBoost OzaBoostAdwin TemporallyAugmentedClassifier WeightedMajorityAlgorithm |
| MOA classification trees | AdaHoeffdingOptionTree ASHoeffdingTree DecisionStump HoeffdingAdaptiveTree HoeffdingOptionTree HoeffdingTree LimAttHoeffdingTree MOA_classification_trees RandomHoeffdingTree |
| Create a MOA classifier | MOA_classifier |
| MOA recommendation engines | BaselinePredictor BRISMFPredictor MOA_recommendation_engines |
| Create a MOA recommendation engine | MOA_recommender |
| Create a MOA regressor | MOA_regressor |
| MOA regressors | AMRulesRegressor FadingTargetMean FIMTDD MOA_regressors ORTO Perceptron TargetMean |
| Define the attributes of a dataset (factor levels, numeric or string data) in a MOA setting | MOAattributes |
| Get and set options for models build with MOA. | MOAoptions |
| Predict using a MOA classifier, MOA regressor or MOA recommender on a new dataset | predict.MOA_trainedmodel |
| Summary statistics of a MOA classifier | summary.MOA_classifier |
| Summary statistics of a MOA recommender | summary.MOA_recommender |
| Summary statistics of a MOA regressor | summary.MOA_regressor |
| Train a MOA classifier/regressor/recommendation engine on a datastream | trainMOA |
| Train a MOA classifier (e.g. a HoeffdingTree) on a datastream | trainMOA.MOA_classifier |
| Train a MOA recommender (e.g. a BRISMFPredictor) on a datastream | trainMOA.MOA_recommender |
| Train a MOA regressor (e.g. a FIMTDD) on a datastream | trainMOA.MOA_regressor |
