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.html , rnn.pdf | 
| Vignettes: | GRU units (source, R code) LSTM units (source, R code) Basic Recurrent Neural Network (source, R code) Recurrent Neural Network (source, R code) RNN units (source, R code) Simple Self-Attention from Scratch (source, R code) Sinus and Cosinus (source, R code) | 
| 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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