Some parts of the code and the documentation copied parts from the R package torch, which comes with the MIT License.

# MIT License

Copyright (c) 2020 Daniel Falbel

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

--------------------------------------------------------------------------------

The TabM learner (`R/LearnerTorchTabM.R`) is a port of the official reference
implementation of TabM, the single-file Python package `tabm.py` (version 0.0.3) from

  Project   : TabM -- Advancing Tabular Deep Learning with Parameter-Efficient
              Ensembling (ICLR 2025)
  URL       : https://github.com/yandex-research/tabm
  Copyright : Copyright 2024 Yandex Research
  License   : Apache License, Version 2.0

Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

--------------------------------------------------------------------------------

The RealMLP learner (`R/LearnerTorchRealMLP.R`) is a port of the official reference
implementation of RealMLP in the Python package `pytabkit` (version 1.7.3) from

  Project   : Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular
              Data (NeurIPS 2024)
  URL       : https://github.com/dholzmueller/pytabkit
  Copyright : Copyright 2024-present David Holzmüller
  License   : Apache License, Version 2.0

Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

--------------------------------------------------------------------------------

The embeddings for numerical features (in `R/LearnerTorchTabM.R`, under the header
"Embeddings for numerical features") are a port of the
official reference implementation, the single-file Python package
`rtdl_num_embeddings.py` (version 0.0.12) from

  Project   : On Embeddings for Numerical Features in Tabular Deep Learning
              (NeurIPS 2022)
  URL       : https://github.com/yandex-research/rtdl-num-embeddings
  Copyright : Copyright (c) 2022 Authors of "On Embeddings for Numerical
              Features in Tabular Deep Learning"
  License   : MIT

# MIT License

Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.