The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 [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 |
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 |
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.