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yap (version 0.1.1)

Yet Another Probabilistic Neural Network

Description

Another implementation of probabilistic neural network in R based on Specht (1990) . It is applicable to the pattern recognition with a N-level response, where N > 2.

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Install

install.packages('yap')

Monthly Downloads

15

Version

0.1.1

License

GPL (>= 2)

Issues

0

Pull Requests

0

Stars

7

Forks

1

Maintainer

WenSui Liu

Last Published

October 25th, 2020

Functions in yap (0.1.1)

pnn.x_imp

Derive the importance of a predictor used in the PNN
pnn.fit

Create a probabilistic neural network
gen_latin

Generate random numbers of latin hypercube sampling
pnn.imp

Derive the importance rank of all predictors used in the PNN
folds

Generate a list of index for the n-fold cross-validation
dummies

Convert a N-category vector to a N-dimension matrix
pnn.predone

Calculate the predicted probability for each category of PNN
pnn.search_logl

Search for the optimal value of PNN smoothing parameter based on the cross entropy
gen_sobol

Generate sobol sequence
pnn.pfi

Derive the PFI rank of all predictors used in the PNN
pnn.predict

Calculate a matrix of predicted probabilities
logl

Calculate the multiclass cross-entropy
gen_unifm

Generate Uniform random numbers
pnn.optmiz_logl

Optimize the optimal value of PNN smoothing parameter based on the cross entropy
pnn.parpred

Calculate predicted probabilities of PNN by using parallelism
pnn.x_pfi

Derive the permutation feature importance of a predictor used in the PNN