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
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URL https://github.com/statcompute/yager
License GPL (>= 2)
Encoding UTF-8
LazyData true
RoxygenNote 7.0.2
NeedsCompilation no
Packaged 2020-01-10 18:07:17 UTC; liuwensui
Repository CRAN
Date/Publication 2020-01-14 10:30:02 UTC

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