introduce pre-commit (#55)

Author: TomeHirata

.pre-commit-config.yaml

@@ -0,0 +1,22 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.12.7
+    hooks:
+      - id: ruff
+        name: ruff (linter)
+        args: [--fix]
+      - id: ruff-format
+        name: ruff (formatter)
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-merge-conflict
+      - id: check-yaml
+      - id: check-toml
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.28.1
+    hooks:
+      - id: typos
+        args: [--write-changes]

docs/source/api_reference.rst

@@ -16,13 +16,13 @@ The dte_adj package provides several types of estimators for computing distribut
 
 For theoretical foundations, see Byambadalai et al. (2024) [#simple2024]_ for simple randomization and Byambadalai et al. (2025) [#car2025]_ for covariate-adaptive randomization.
 
-For multi-task learning approaches that train models for all locations simultaneously (using ``is_multi_task=True``), see the neural network framework in [#multitask2024]_.
+For multi-task learning approaches that train models for all locations simultaneously (using ``is_multi_task=True``), see the neural network framework in [#multitask2025]_.
 
 .. [#simple2024] Byambadalai, U., Oka, T., & Yasui, S. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. arXiv preprint `arXiv:2407.16037 <https://arxiv.org/abs/2407.16037>`_.
 
 .. [#car2025] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. arXiv preprint `arXiv:2506.05945 <https://arxiv.org/abs/2506.05945>`_.
 
-.. [#multitask2024] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
+.. [#multitask2025] Hirata, T., Byambadalai, U., Oka, T., Yasui, S., & Uto, S. (2025). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
 
 Detailed Documentation
 ----------------------

docs/source/index.rst

@@ -29,13 +29,13 @@ For theoretical foundations, see:
 
 * **Simple randomization**: Byambadalai et al. (2024) [#simple2024]_
 * **Covariate-adaptive randomization**: Byambadalai et al. (2025) [#car2025]_
-* **Multi-task learning**: Byambadalai et al. (2024) [#multitask2024]_
+* **Multi-task learning**: Hirata et al. (2025) [#multitask2025]_
 
 .. [#simple2024] Byambadalai, U., Oka, T., & Yasui, S. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. arXiv preprint `arXiv:2407.16037 <https://arxiv.org/abs/2407.16037>`_.
 
 .. [#car2025] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. arXiv preprint `arXiv:2506.05945 <https://arxiv.org/abs/2506.05945>`_.
 
-.. [#multitask2024] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
+.. [#multitask2025] Hirata, T., Byambadalai, U., Oka, T., Yasui, S., & Uto, S. (2025). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint `arXiv:2507.07738 <https://arxiv.org/abs/2507.07738>`_.
 
 .. toctree::
    :maxdepth: 1