Package: nnetsauce 0.50.1

T. Moudiki

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

Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Authors:T. Moudiki

nnetsauce_0.50.1.tar.gz
nnetsauce_0.50.1.zip(r-4.7-any)nnetsauce_0.50.1.zip(r-4.6-any)nnetsauce_0.50.1.zip(r-4.5-any)
nnetsauce_0.50.1.tgz(r-4.6-any)nnetsauce_0.50.1.tgz(r-4.5-any)
nnetsauce_0.50.1.tar.gz(r-4.7-any)nnetsauce_0.50.1.tar.gz(r-4.6-any)
nnetsauce_0.50.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
nnetsauce/json (API)

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

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

On CRAN:

Conda:

deep-learningmachine-learningneural-networksrandomized-algorithmsstatistical-learning

2.90 score 4 stars 10 scripts 29 exports 42 dependencies

Last updated from:698dae1cb0. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develWARNING1165
source / vignettesOK259
linux-releaseWARNING1173
macos-releaseWARNING1146
macos-oldrelWARNING1098
windows-develWARNING2598
windows-releaseWARNING2094
windows-oldrelWARNING1645
wasm-releaseOK92

Exports:AdaBoostClassifierBaseRegressorBayesianRVFL2RegressorBayesianRVFLRegressorCustomClassifierCustomRegressorDeepClassifierDeepMTSDeepRegressorget_nsget_sklearnGLMClassifierGLMRegressorLazyClassifierLazyDeepClassifierLazyDeepMTSLazyDeepRegressorLazyMTSLazyRegressorMTSMultitaskClassifierplot.MTSQuantileRegressorRandomBagClassifierRandomBagRegressorRidge2ClassifierRidge2MultitaskClassifierRidge2Regressorto_forecast

Dependencies:cachemclicolorspacecpp11farverfastmapforecastfracdiffgenericsggplot2gluegtablehereisobandjsonlitelabelinglatticelifecyclelmtestmagrittrMatrixmemoisenlmennetpngR6rappdirsRColorBrewerRcppRcppArmadilloRcppTOMLreticulaterlangrprojrootS7scalestimeDateurcavctrsviridisLitewithrzoo

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
Model-agnostic quantile regressorQuantileRegressor
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
Transform list to forecast or mforecast objectto_forecast