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Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al., <doi:10.48550/arXiv.1412.3555>) and vanilla RNN.
Version: | 1.9.0 |
Depends: | R (≥ 3.2.2) |
Imports: | attention, sigmoid (≥ 1.4.0) |
Suggests: | testthat, knitr, rmarkdown |
Published: | 2023-04-22 |
DOI: | 10.32614/CRAN.package.rnn |
Author: | Bastiaan Quast |
Maintainer: | Bastiaan Quast <bquast at gmail.com> |
BugReports: | https://github.com/bquast/rnn/issues |
License: | GPL-3 |
URL: | https://qua.st/rnn/, https://github.com/bquast/rnn |
NeedsCompilation: | no |
Citation: | rnn citation info |
Materials: | README NEWS |
CRAN checks: | rnn results |
Reference manual: | rnn.pdf |
Vignettes: |
GRU units LSTM units Basic Recurrent Neural Network Recurrent Neural Network RNN units Simple Self-Attention from Scratch Sinus and Cosinus |
Package source: | rnn_1.9.0.tar.gz |
Windows binaries: | r-devel: rnn_1.9.0.zip, r-release: rnn_1.9.0.zip, r-oldrel: rnn_1.9.0.zip |
macOS binaries: | r-release (arm64): rnn_1.9.0.tgz, r-oldrel (arm64): rnn_1.9.0.tgz, r-release (x86_64): rnn_1.9.0.tgz, r-oldrel (x86_64): rnn_1.9.0.tgz |
Old sources: | rnn archive |
Reverse imports: | SLBDD |
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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.