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yager (version 0.1.1)

Yet Another General Regression Neural Network

Description

Another implementation of general regression neural network in R based on Specht (1991) . It is applicable to the functional approximation or the classification.

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Install

install.packages('yager')

Monthly Downloads

95

Version

0.1.1

License

GPL (>= 2)

Issues

0

Pull Requests

0

Stars

11

Forks

4

Maintainer

WenSui Liu

Last Published

October 25th, 2020

Functions in yager (0.1.1)

grnn.imp

Derive the importance rank of all predictors used in the GRNN
grnn.search_rsq

Search for the optimal value of GRNN smoothing parameter based on r-square
grnn.x_imp

Derive the importance of a predictor used in the GRNN
grnn.x_pfi

Derive the permutation feature importance of a predictor used in the GRNN
grnn.parpred

Calculate predicted values of GRNN by using parallelism
grnn.partial

Derive the partial effect of a predictor used in a GRNN
gen_unifm

Generate Uniform random numbers
grnn.margin

Derive the marginal effect of a predictor used in a GRNN
grnn.pfi

Derive the PFI rank of all predictors used in the GRNN
gen_latin

Generate random numbers of latin hypercube sampling
grnn.predict

Calculate predicted values of GRNN
grnn.optmiz_auc

Optimize the optimal value of GRNN smoothing parameter based on AUC
folds

Generate a list of index for the n-fold cross-validation
grnn.predone

Calculate a predicted value of GRNN
grnn.search_auc

Search for the optimal value of GRNN smoothing parameter based on AUC
gen_sobol

Generate sobol sequence
grnn.fit

Create a general regression neural network