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'))

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

On CRAN:

deep-learning machine-learning neural-networks randomized-algorithms statistical-learning

2.90 score 2 stars 6 scripts 28 exports 54 dependencies

Last updated 3 months agofrom:cd49393277. Checks:OK: 1 ERROR: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 26 2024
R-4.5-win	ERROR	Oct 26 2024
R-4.5-linux	ERROR	Oct 26 2024
R-4.4-win	ERROR	Oct 26 2024
R-4.4-mac	ERROR	Oct 26 2024
R-4.3-win	ERROR	Oct 26 2024
R-4.3-mac	ERROR	Oct 26 2024

Exports:AdaBoostClassifier BaseRegressor BayesianRVFL2Regressor BayesianRVFLRegressor CustomClassifier CustomRegressor DeepClassifier DeepMTS DeepRegressor GLMClassifier GLMRegressor LazyClassifier LazyDeepClassifier LazyDeepMTS LazyDeepRegressor LazyMTS LazyRegressor MTS MultitaskClassifier ns plot.MTS RandomBagClassifier RandomBagRegressor Ridge2Classifier Ridge2MultitaskClassifier Ridge2Regressor sklearn to_forecast

Dependencies:cachem cli colorspace curl fansi farver fastmap forecast fracdiff generics ggplot2 glue gtable here isoband jsonlite labeling lattice lifecycle lmtest magrittr MASS Matrix memoise mgcv munsell nlme nnet pillar pkgconfig png quadprog quantmod R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppTOML reticulate rlang rprojroot scales tibble timeDate tseries TTR urca utf8 vctrs viridisLite withr xts zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Adaboost classifier with quasi-randomized hidden layer	AdaBoostClassifier
Linear regressor with a quasi-randomized layer	BaseRegressor
Bayesian Random Vector Functional link network with 2 shrinkage parameters	BayesianRVFL2Regressor
Bayesian Random Vector Functional link network with 1 shrinkage parameter	BayesianRVFLRegressor
Custom classifier with quasi-randomized layer	CustomClassifier
Custom regressor with quasi-randomized layer	CustomRegressor
Deep classification models	DeepClassifier
Deep MTS models	DeepMTS
Deep regression models	DeepRegressor
Generalized nonlinear models for Classification	GLMClassifier
Generalized nonlinear models for continuous output (regression)	GLMRegressor
Automated Machine Learning for classification models	LazyClassifier
Automated Machine Learning for deep classification models	LazyDeepClassifier
Automated Machine Learning for deep time series models	LazyDeepMTS
Automated Machine Learning for deep regression models	LazyDeepRegressor
Automated Machine Learning for time series models	LazyMTS
Automated Machine Learning for regression models	LazyRegressor
Multivariate Time Series	MTS
Multitask Classification model based on regression models, with shared covariates	MultitaskClassifier
Plot multivariate time series forecast or residuals	plot.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 parameters	Ridge2Classifier
Multitask quasi-randomized classification model with 2 shrinkage parameters	Ridge2MultitaskClassifier
Quasi-randomized regression model with 2 shrinkage parameters	Ridge2Regressor