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
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


Reference manual: spnn.pdf


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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