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: spnn_1.2.1.tgz, r-oldrel: spnn_1.2.1.tgz |
| Old sources: | spnn archive |
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