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:

Reference manual:	einops.html , einops.pdf
Vignettes:	basics (source, R code)

Downloads:

Package source:	einops_0.2.1.tar.gz
Windows binaries:	r-devel: einops_0.2.1.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): einops_0.2.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): einops_0.2.1.tgz, r-oldrel (x86_64): einops_0.2.1.tgz

Linking:

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