# yager v0.1.0

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## Yet Another General Regression Neural Network

Another implementation of general regression neural network in R based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the functional approximation or the classification.

## Functions in yager

 Name Description 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 No Results!