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This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.
| Version: | 1.2.2 |
| Imports: | FNN, stringi, beepr, ggplot2, keras, dplyr, readr, parallel, tm, e1071, SnowballC, data.table, fastmatch, neuralnet |
| Suggests: | textclean |
| Published: | 2022-04-27 |
| DOI: | 10.32614/CRAN.package.LilRhino |
| Author: | Travis Barton (2018) |
| Maintainer: | Travis Barton <travisdatabarton at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | LilRhino results |
| Reference manual: | LilRhino.pdf |
| Package source: | LilRhino_1.2.2.tar.gz |
| Windows binaries: | r-devel: LilRhino_1.2.2.zip, r-release: LilRhino_1.2.2.zip, r-oldrel: LilRhino_1.2.2.zip |
| macOS binaries: | r-release (arm64): LilRhino_1.2.2.tgz, r-oldrel (arm64): LilRhino_1.2.2.tgz, r-release (x86_64): LilRhino_1.2.2.tgz, r-oldrel (x86_64): LilRhino_1.2.2.tgz |
| Old sources: | LilRhino archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.