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rnn: Recurrent Neural Network

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 ORCID iD [aut, cre]
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

Documentation:

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

Downloads:

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 dependencies:

Reverse imports: SLBDD

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rnn to link to this page.

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.