Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
Version: 0.15.1 Imports: Rcpp, R6, withr, rlang (≥ 1.0.0), methods, utils, stats, bit64, magrittr, tools, coro (≥ 1.0.2), callr, cli (≥ 3.0.0), glue, desc, safetensors (≥ 0.1.1), jsonlite, scales LinkingTo: Rcpp Suggests: testthat (≥ 3.0.0), covr, knitr (≥ 1.36), rmarkdown, palmerpenguins, mvtnorm, numDeriv, katex Published: 2025-07-10 DOI: 10.32614/CRAN.package.torch Author: Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], Hamada S. Badr [ctb], Sebastian Fischer [ctb], Maximilian Pichler [ctb], RStudio [cph] Maintainer: Daniel Falbel <daniel at rstudio.com> BugReports: https://github.com/mlverse/torch/issues License: MIT + file LICENSE URL: https://torch.mlverse.org/docs, https://github.com/mlverse/torch NeedsCompilation: yes SystemRequirements: LibTorch (https://pytorch.org/); Only x86_64 platforms are currently supported except for ARM system running macOS. Materials: README NEWS In views: MachineLearning CRAN checks: torch results Documentation: Reference manual: torch.pdf Vignettes: Distributions (source, R code)Please use the canonical form https://CRAN.R-project.org/package=torch to link to this page.
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