einops: Flexible, Powerful, and Readable Tensor Operations
Perform tensor operations using a concise yet expressive syntax inspired by the Python library of the same name.
Reshape, rearrange, and combine multidimensional arrays for scientific computing, machine learning, and data analysis.
Einops simplifies complex manipulations, making code more maintainable and intuitive.
The original implementation is demonstrated in Rogozhnikov (2022) <https://openreview.net/forum?id=oapKSVM2bcj>.
| Version: |
0.2.1 |
| Depends: |
R (≥ 3.5) |
| Imports: |
assertthat, FastUtils, glue, magrittr, r2r, R6, roperators |
| Suggests: |
abind, grid, imager, knitr, lifecycle, lintr, lobstr, rmarkdown, spelling, testthat (≥ 3.0.0), torch, zeallot |
| Published: |
2025-09-03 |
| DOI: |
10.32614/CRAN.package.einops |
| Author: |
Qile Yang  [cre,
aut, cph] |
| Maintainer: |
Qile Yang <qile.yang at berkeley.edu> |
| BugReports: |
https://github.com/Qile0317/einops/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/Qile0317/einops,
https://qile0317.github.io/einops/ |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
einops citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
einops results |
Documentation:
Downloads:
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