spnn: Scale Invariant Probabilistic Neural Networks

Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.

Version:	1.2.1
Imports:	MASS (≥ 3.1-20), Rcpp (≥ 1.0.0)
LinkingTo:	Rcpp, RcppArmadillo
Published:	2020-01-08
DOI:	10.32614/CRAN.package.spnn
Author:	Romin Ebrahimi
Maintainer:	Romin Ebrahimi <romin.ebrahimi at utexas.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	spnn results

Documentation:

Reference manual:

spnn.pdf

Downloads:

Package source:	spnn_1.2.1.tar.gz
Windows binaries:	r-devel: spnn_1.2.1.zip, r-release: spnn_1.2.1.zip, r-oldrel: spnn_1.2.1.zip
macOS binaries:	r-release (arm64): spnn_1.2.1.tgz, r-oldrel (arm64): spnn_1.2.1.tgz, r-release (x86_64): spnn_1.2.1.tgz, r-oldrel (x86_64): spnn_1.2.1.tgz
Old sources:	spnn archive

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