R: Yet Another General Regression Neural Network

# Yet Another General Regression Neural Network

## Documentation for package ‘yager’ version 0.1.1

- DESCRIPTION file

## Help Pages

folds | Generate a list of index for the n-fold cross-validation
gen_latin | Generate random numbers of latin hypercube sampling
gen_sobol | Generate sobol sequence
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.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
grnn.parpred | Calculate predicted values of GRNN by using parallelism
grnn.partial | Derive the partial effect of a predictor used in a GRNN
grnn.pfi | Derive the PFI rank of all predictors used in the GRNN
grnn.predict | Calculate predicted values of GRNN
grnn.predone | Calculate a predicted value of GRNN
grnn.search_auc | Search for the optimal value of GRNN smoothing parameter based on AUC
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