Learn R Programming
⚠️There's a newer version (0.1.1) of this package.Take me there.

yager (version 0.1.0)

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.

Copy Link

Version

Install

install.packages('yager')

Monthly Downloads

96

Version

0.1.0

License

GPL (>= 2)

Issues

0

Pull Requests

0

Stars

11

Forks

4

Maintainer

WenSui Liu

Last Published

January 14th, 2020

Functions in yager (0.1.0)

gen_sobol

Generate sobol sequence
folds

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

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

Generate random numbers of latin hypercube sampling
grnn.predone

Calculate a predicted value of GRNN
grnn.x_pfi

Derive the permutation feature importance of a predictor used in the GRNN
gen_unifm

Generate Uniform random numbers
grnn.fit

Create a general regression neural network
grnn.imp

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

Derive the PFI rank of all predictors used in the GRNN
grnn.predict

Calculate predicted values of GRNN
grnn.partial

Derive the partial effect of a predictor used in a GRNN
grnn.x_imp

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

Calculate predicted values of GRNN by using parallelism
grnn.search_rsq

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

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

Optimize the optimal value of GRNN smoothing parameter based on AUC