DESCRIPTION

Package: torch
Type: Package
Title: Tensors and Neural Networks with 'GPU' Acceleration
Version: 0.8.1.9000
Authors@R: c(
    person("Daniel", "Falbel", email = "daniel@rstudio.com", role = c("aut", "cre", "cph")),
    person("Javier", "Luraschi", email = "jluraschi@gmail.com", role = c("aut")),
    person("Dmitriy", "Selivanov", role = c("ctb")),
    person("Athos", "Damiani", role = c("ctb")),
    person("Christophe", "Regouby", role = c("ctb")),
    person("Krzysztof", "Joachimiak", role = c("ctb")),
    person("Hamada S.", "Badr", role = c("ctb")),
    person(family = "RStudio", role = c("cph"))
    )
Description: Provides functionality to define and train neural networks similar to
    'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R
    using the 'libtorch' library. Also supports low-level tensor operations and
    'GPU' acceleration.
License: MIT + file LICENSE
URL: https://torch.mlverse.org/docs, https://github.com/mlverse/torch
BugReports: https://github.com/mlverse/torch/issues
Encoding: UTF-8
SystemRequirements: C++11, LibTorch (https://pytorch.org/); Only x86_64 platforms
  are currently supported.
LinkingTo:
    Rcpp
Imports:
    Rcpp,
    R6,
    withr,
    rlang,
    methods,
    utils,
    stats,
    bit64,
    magrittr,
    tools,
    coro (>= 1.0.2),
    callr,
    cli,
    ellipsis
RoxygenNote: 7.2.1
Roxygen: list(markdown = TRUE)
Suggests:
    testthat (>= 3.0.0),
    covr,
    knitr (>= 1.36),
    rmarkdown,
    glue,
    palmerpenguins,
    mvtnorm,
    numDeriv,
    katex
VignetteBuilder: knitr
Collate:
    'R7.R'
    'RcppExports.R'
    'tensor.R'
    'autograd.R'
    'backends.R'
    'call_torch_function.R'
    'codegen-utils.R'
    'compat-purrr.R'
    'compilation_unit.R'
    'conditions.R'
    'contrib.R'
    'creation-ops.R'
    'cuda.R'
    'device.R'
    'dimname_list.R'
    'utils.R'
    'distributions-constraints.R'
    'distributions-utils.R'
    'distributions-exp-family.R'
    'distributions.R'
    'distributions-bernoulli.R'
    'distributions-categorical.R'
    'distributions-gamma.R'
    'distributions-chi2.R'
    'distributions-mixture_same_family.R'
    'distributions-multivariate_normal.R'
    'distributions-normal.R'
    'distributions-poisson.R'
    'dtype.R'
    'gen-method.R'
    'gen-namespace-docs.R'
    'gen-namespace-examples.R'
    'gen-namespace.R'
    'generator.R'
    'help.R'
    'indexing.R'
    'install.R'
    'ivalue.R'
    'jit-compile.R'
    'lantern_load.R'
    'lantern_sync.R'
    'layout.R'
    'linalg.R'
    'memory_format.R'
    'utils-data.R'
    'nn.R'
    'nn-activation.R'
    'nn-batchnorm.R'
    'nn-conv.R'
    'nn-distance.R'
    'nn-dropout.R'
    'nn-flatten.R'
    'nn-init.R'
    'nn-linear.R'
    'nn-loss.R'
    'nn-normalization.R'
    'nn-pooling.R'
    'nn-rnn.R'
    'nn-sparse.R'
    'nn-upsampling.R'
    'nn-utils-clip-grad.R'
    'nn-utils-rnn.R'
    'nn-utils.R'
    'nn_adaptive.R'
    'nnf-activation.R'
    'nnf-batchnorm.R'
    'nnf-conv.R'
    'nnf-distance.R'
    'nnf-dropout.R'
    'nnf-embedding.R'
    'nnf-fold.R'
    'nnf-instancenorm.R'
    'nnf-linear.R'
    'nnf-loss.R'
    'nnf-normalization.R'
    'nnf-padding.R'
    'nnf-pixelshuffle.R'
    'nnf-pooling.R'
    'nnf-upsampling.R'
    'nnf-vision.R'
    'operators.R'
    'optim.R'
    'optim-adadelta.R'
    'optim-adagrad.R'
    'optim-adam.R'
    'optim-asgd.R'
    'optim-lbfgs.R'
    'optim-lr_scheduler.R'
    'optim-rmsprop.R'
    'optim-rprop.R'
    'optim-sgd.R'
    'package.R'
    'qscheme.R'
    'quantization.R'
    'reduction.R'
    'save.R'
    'scalar.R'
    'script_module.R'
    'stack.R'
    'storage.R'
    'tensor_options.R'
    'threads.R'
    'trace.R'
    'translate.R'
    'type-info.R'
    'utils-data-collate.R'
    'utils-data-dataloader.R'
    'utils-data-enum.R'
    'utils-data-fetcher.R'
    'utils-data-sampler.R'
    'utils-pipe.R'
    'variable_list.R'
    'with-indices.R'
    'wrapers.R'