@@ -0,0 +1,37 @@
+name: publish
+
+on:
+  push:
+    tags: ["v*"]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+      - name: Build sdist + wheel
+        run: |
+          python -m pip install --upgrade pip build
+          python -m build
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+  publish:
+    needs: build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/project/jet-array/
+    permissions:
+      id-token: write
+    steps:
+      - uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: dist/
+      - uses: pypa/gh-action-pypi-publish@release/v1
@@ -0,0 +1,27 @@
+name: tests
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: pip
+      - name: Install
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e ".[test]"
+      - name: Run tests
+        run: pytest -v
@@ -0,0 +1,20 @@
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+build/
+dist/
+.eggs/
+.pytest_cache/
+.coverage
+.coverage.*
+htmlcov/
+.tox/
+.venv/
+venv/
+env/
+.python-version
+.DS_Store
+.idea/
+.vscode/
+*.swp
@@ -0,0 +1,202 @@
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@@ -0,0 +1,92 @@
+# jet-array
+
+Array-form Taylor-mode automatic differentiation in JAX.
+
+`jet_array` propagates truncated Taylor polynomials through arbitrary JAX
+computations, returning all coefficients up to a specified order in a single
+forward pass. This is the higher-order analogue of `jax.jvp`: where `jvp`
+takes a primal and one tangent and returns one derivative, `jet` takes a
+primal and a series of tangents and returns the full Taylor expansion.
+
+This package is a derivative of JAX's experimental `jax.experimental.jet`
+([Bettencourt, Johnson, Duvenaud 2019][taylor-mode]) with the series stored as
+a single leading-axis array rather than a Python tuple. The array layout
+enables `jit`/`vmap`/`scan` over the order axis, and adds an
+`effective_order` parameter for dynamic computation depth at static shapes —
+useful when the truncation order varies across a batch.
+
+## Install
+
+```bash
+pip install jet-array
+```
+
+`jet_array` uses `jax._src` internals and is currently pinned to
+`jax>=0.8,<0.9`. Compatibility across JAX versions will be tightened as the
+package matures.
+
+## Quickstart
+
+```python
+import jax.numpy as jnp
+from jet_array import jet
+
+def f(x):
+    return jnp.exp(jnp.sin(x))
+
+# 5th-order Taylor series of f at x=0.5, with input series [1, 0, 0, 0, 0]
+# (i.e. expand around x = 0.5 + t).
+x0 = 0.5
+series_in = jnp.zeros(5).at[0].set(1.0)
+
+primal_out, series_out = jet(f, (x0,), (series_in,))
+# primal_out  = f(0.5)
+# series_out  = [f'(0.5), f''(0.5)/2!, f'''(0.5)/3!, f^(4)(0.5)/4!, f^(5)(0.5)/5!]
+```
+
+## Why arrays instead of tuples
+
+`jax.experimental.jet` stores the series as a Python tuple, which prevents
+`jit`/`vmap` from treating the order axis as a regular array dimension. This
+matters when:
+
+- The series order is large (hundreds of coefficients) — Python-level
+  iteration becomes a tracing-time cost.
+- The order is data-dependent — tuples force re-tracing every time the
+  length changes.
+- You want to `vmap` or `scan` over a batch where each element has its own
+  effective order.
+
+`jet_array` stores all coefficients along axis 0 of a single array,
+addressing all three.
+
+## Citation
+
+If you use `jet-array` in academic work, please cite:
+
+```bibtex
+@misc{yang2026copulaad,
+  title={Archimedean Copula Inference via Taylor-Mode AD},
+  author={Yang, Cambridge and Li, Dongdong},
+  year={2026},
+  note={arXiv preprint},
+}
+```
+
+The underlying Taylor-mode algorithm is from:
+
+```bibtex
+@inproceedings{bettencourt2019taylor,
+  title={Taylor-mode automatic differentiation for higher-order derivatives in JAX},
+  author={Bettencourt, Jesse and Johnson, Matthew J. and Duvenaud, David},
+  booktitle={NeurIPS Program Transformations Workshop},
+  year={2019},
+}
+```
+
+## License
+
+Apache-2.0. Portions derived from JAX (Apache-2.0, Copyright 2020 The JAX
+Authors); copyright headers preserved per Apache-2.0 §4.
+
+[taylor-mode]: https://github.com/jax-ml/jax/files/6717197/jet.pdf