Package: nnetsauce 0.20.6

T. Moudiki

nnetsauce: Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Authors:T. Moudiki

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

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

Peer review:

Bug tracker:https://github.com/techtonique/nnetsauce_r/issues

On CRAN:

deep-learningmachine-learningneural-networksrandomized-algorithmsstatistical-learning

28 exports 1 stars 1.00 score 54 dependencies

Last updated 2 months agofrom:78242c2fa5

Exports:AdaBoostClassifierBaseRegressorBayesianRVFL2RegressorBayesianRVFLRegressorCustomClassifierCustomRegressorDeepClassifierDeepMTSDeepRegressorGLMClassifierGLMRegressorLazyClassifierLazyDeepClassifierLazyDeepMTSLazyDeepRegressorLazyMTSLazyRegressorMTSMultitaskClassifiernsplot.MTSRandomBagClassifierRandomBagRegressorRidge2ClassifierRidge2MultitaskClassifierRidge2Regressorsklearnto_forecast

Dependencies:cachemclicolorspacecurlfansifarverfastmapforecastfracdiffgenericsggplot2gluegtablehereisobandjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmemoisemgcvmunsellnlmennetpillarpkgconfigpngquadprogquantmodR6rappdirsRColorBrewerRcppRcppArmadilloRcppTOMLreticulaterlangrprojrootscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo

Readme and manuals

Help Manual

Help pageTopics
Adaboost classifier with quasi-randomized hidden layerAdaBoostClassifier
Linear regressor with a quasi-randomized layerBaseRegressor
Bayesian Random Vector Functional link network with 2 shrinkage parametersBayesianRVFL2Regressor
Bayesian Random Vector Functional link network with 1 shrinkage parameterBayesianRVFLRegressor
Custom classifier with quasi-randomized layerCustomClassifier
Custom regressor with quasi-randomized layerCustomRegressor
Deep classification modelsDeepClassifier
Deep MTS modelsDeepMTS
Deep regression modelsDeepRegressor
Generalized nonlinear models for ClassificationGLMClassifier
Generalized nonlinear models for continuous output (regression)GLMRegressor
Automated Machine Learning for classification modelsLazyClassifier
Automated Machine Learning for deep classification modelsLazyDeepClassifier
Automated Machine Learning for deep time series modelsLazyDeepMTS
Automated Machine Learning for deep regression modelsLazyDeepRegressor
Automated Machine Learning for time series modelsLazyMTS
Automated Machine Learning for regression modelsLazyRegressor
Multivariate Time SeriesMTS
Multitask Classification model based on regression models, with shared covariatesMultitaskClassifier
Plot multivariate time series forecast or residualsplot.MTS
Bootstrap aggregating with quasi-randomized layer (classification)RandomBagClassifier
Bootstrap aggregating with quasi-randomized layer (regression)RandomBagRegressor
Multinomial logit, quasi-randomized classification model with 2 shrinkage parametersRidge2Classifier
Multitask quasi-randomized classification model with 2 shrinkage parametersRidge2MultitaskClassifier
Quasi-randomized regression model with 2 shrinkage parametersRidge2Regressor