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This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse identification algorithm for finding ordinary differential equations for a measured system from raw data (SINDy). The package includes a set of additional tools for working with raw data, with an emphasis on cognitive science applications (Dale and Bhat, 2018 <doi:10.1016/j.cogsys.2018.06.020>). See <https://github.com/racdale/sindyr> for examples and updates.
| Version: | 0.2.4 |
| Depends: | R (≥ 3.4), arrangements, matrixStats, igraph, graphics, grDevices |
| Imports: | pracma |
| Published: | 2024-05-01 |
| Author: | Rick Dale and Harish S. Bhat |
| Maintainer: | Rick Dale <racdale at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | sindyr results |
| Reference manual: | sindyr.pdf |
| Package source: | sindyr_0.2.4.tar.gz |
| Windows binaries: | r-devel: sindyr_0.2.4.zip, r-release: sindyr_0.2.4.zip, r-oldrel: sindyr_0.2.4.zip |
| macOS binaries: | r-release (arm64): sindyr_0.2.4.tgz, r-oldrel (arm64): sindyr_0.2.4.tgz, r-release (x86_64): sindyr_0.2.4.tgz, r-oldrel (x86_64): sindyr_0.2.4.tgz |
| Old sources: | sindyr 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.
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