From 8a4d182fadb6d6064cecd1f86b43cbd1fac30688 Mon Sep 17 00:00:00 2001
From: Jan Zill <jan@outerloop.io>
Date: Tue, 13 Oct 2026 16:08:18 +1000
Subject: [PATCH 1/4] add py314 support, remove 3.10. Contains temporary local
 checkout for sharrow, remoev once py314 is merged.

---
 .github/workflows/ci.yml          |  103 +-
 .github/workflows/docs.yml        |    2 +-
 .github/workflows/release.yml     |    2 +-
 .python-version                   |    2 +-
 envs/activitysim/environment.yaml |    4 +-
 envs/arboretum.yml                |    2 +-
 envs/development.yaml             |    2 +-
 envs/uv-activitysim.sh            |    2 +-
 envs/windows.yaml                 |    2 +-
 pyproject.toml                    |   24 +-
 src/larch/model/constraints.py    |    6 +
 src/larch/model/gradient_check.py |    5 +-
 src/larch/model/numbamodel.py     |    5 +-
 src/larch/model/optimization.py   |    9 +-
 src/larch/optimize.py             |    8 +-
 tests/test_vehicle.py             |    9 +-
 uv.lock                           | 1817 ++++++++++++-----------------
 17 files changed, 916 insertions(+), 1088 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 762c4489..97463ef6 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -6,6 +6,10 @@ on:
     branches: [ main ]
   workflow_dispatch:
 
+env:
+  # Temporary until Sharrow publishes its Python 3.14 AST compatibility fixes.
+  SHARROW_REVISION: 56a41c760d020537dc551afa946ebf2f83d1c85a
+
 jobs:
   test-dev-install:
     name: developer install
@@ -13,17 +17,36 @@ jobs:
     defaults:
       run:
         shell: bash -l {0}
+        working-directory: larch
     steps:
         - name: checkout
           uses: actions/checkout@v4
+          with:
+            path: larch
+        - name: checkout compatible Sharrow
+          uses: actions/checkout@v4
+          with:
+            repository: outerl/sharrow
+            ref: ${{ env.SHARROW_REVISION }}
+            path: sharrow
+        - name: Install uv
+          uses: astral-sh/setup-uv@v3
+          with:
+            enable-cache: true
+        - name: Set up Python
+          uses: actions/setup-python@v5
+          with:
+            python-version-file: "larch/.python-version"
         - name: setup graphviz
           uses: ts-graphviz/setup-graphviz@v2
-        - name: run developer install script
-          run: bash envs/uv-dev.sh -d .
+        - name: install developer dependencies
+          run: uv sync --locked --all-extras
+        - name: generate example modules
+          run: uv run --no-sync python tools/rip_examples.py
         - name: review environment
           run: uv pip list
         - name: run tests
-          run: uv run pytest
+          run: uv run --no-sync pytest
 
   build-wheel:
     name: 🛞 build wheel
@@ -37,7 +60,6 @@ jobs:
         with:
           fetch-depth: 0
           fetch-tags: true
-          submodules: recursive
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
@@ -45,41 +67,100 @@ jobs:
       - name: "Set up Python"
         uses: actions/setup-python@v5
         with:
-          python-version-file: "pyproject.toml"
+          python-version: "3.14"
       - name: build wheel
-        run: uv build
+        run: |
+          python tools/rip_examples.py
+          uv build --no-sources
       - name: Upload wheel artifact
         uses: actions/upload-artifact@v4
         with:
           name: wheels
           path: dist
 
+  build-sharrow-wheel:
+    name: 🛞 build compatible Sharrow wheel
+    runs-on: ubuntu-latest
+    steps:
+      - name: checkout compatible Sharrow
+        uses: actions/checkout@v4
+        with:
+          repository: outerl/sharrow
+          ref: ${{ env.SHARROW_REVISION }}
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.14"
+      - name: build dependency wheel
+        env:
+          SETUPTOOLS_SCM_PRETEND_VERSION_FOR_SHARROW: 2.16.2+py314.56a41c7
+        run: uv build --wheel --no-sources
+      - name: Upload dependency wheel
+        uses: actions/upload-artifact@v4
+        with:
+          name: sharrow-py314-wheel
+          path: dist/*.whl
+
   test-wheel:
     name: 🧪 test wheel
-    needs: build-wheel
+    needs: [build-wheel, build-sharrow-wheel]
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.10', '3.11', '3.12', '3.13' ]
+        python-version: [ '3.11', '3.12', '3.13', '3.14' ]
+      fail-fast: false
     defaults:
       run:
         shell: bash -l {0}
     steps:
+      - name: checkout tests
+        uses: actions/checkout@v4
       - name: Install uv
         uses: astral-sh/setup-uv@v3
       - name: "Set up Python"
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
+      - name: setup graphviz
+        uses: ts-graphviz/setup-graphviz@v2
       - name: Download wheel artifact
         uses: actions/download-artifact@v4
         with:
           name: wheels
           path: dist
+      - name: Download compatible Sharrow wheel
+        if: matrix.python-version == '3.14'
+        uses: actions/download-artifact@v4
+        with:
+          name: sharrow-py314-wheel
+          path: dependencies
       - name: Install wheel
         run: |
-          uv venv
-          uv pip install dist/*.whl
+          uv venv --python "${{ matrix.python-version }}"
+          wheels=(dist/*.whl)
+          dependency_wheels=()
+          if [[ "${{ matrix.python-version }}" == "3.14" ]]; then
+            dependency_wheels=(dependencies/*.whl)
+          fi
+          uv pip install --only-binary :all: "${wheels[0]}[test]" "${dependency_wheels[@]}"
+          uv pip check
+      - name: Verify installed wheel
+        run: |
+          .venv/bin/python - <<'PY'
+          from pathlib import Path
+          import sys
+          import larch
+          from sharrow.aster import expression_for_numba
+
+          assert Path(larch.__file__).is_relative_to(Path(sys.prefix))
+          assert larch.examples.ex, "Generated examples are missing from the wheel"
+          expression_for_numba(
+              "skims['DIST']", spacename="skims", dim_slots=(0,), spacevars={"DIST"}
+          )
+          print(larch.versions())
+          PY
       - name: Run tests
         run: |
-          uv run python -c "import larch; print(larch.versions())"
+          .venv/bin/python -m pytest tests -o addopts=''
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 1822358f..148bf428 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -25,7 +25,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Build wheel
         run: |
           python tools/rip_examples.py
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index dc0c73c3..44060176 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -25,7 +25,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Build wheel
         run: |
           python tools/rip_examples.py
diff --git a/.python-version b/.python-version
index 24ee5b1b..6324d401 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.13
+3.14
diff --git a/envs/activitysim/environment.yaml b/envs/activitysim/environment.yaml
index ea209e0d..8042d481 100644
--- a/envs/activitysim/environment.yaml
+++ b/envs/activitysim/environment.yaml
@@ -2,7 +2,7 @@ channels:
   - conda-forge
   - nodefaults
 dependencies:
-  - python=3.10
+  - python=3.11
   - pip
   - addicty
   - altair
@@ -32,7 +32,7 @@ dependencies:
   - numpy>=1.26
   - openmatrix
   - openpyxl
-  - pandas=1.4.*
+  - pandas>=2.1
   - pathspec
   - pillow
   - platformdirs
diff --git a/envs/arboretum.yml b/envs/arboretum.yml
index b96fcaeb..f09ebd18 100644
--- a/envs/arboretum.yml
+++ b/envs/arboretum.yml
@@ -4,7 +4,7 @@ channels:
   - conda-forge
   - nodefaults
 dependencies:
-  - python=3.10
+  - python=3.11
   - pip
   - addicty
   - dask
diff --git a/envs/development.yaml b/envs/development.yaml
index 987ceffc..d653c9fc 100644
--- a/envs/development.yaml
+++ b/envs/development.yaml
@@ -2,7 +2,7 @@ channels:
   - conda-forge
   - nodefaults
 dependencies:
-  - python=3.10
+  - python=3.11
   - pip
   - addicty
   - altair
diff --git a/envs/uv-activitysim.sh b/envs/uv-activitysim.sh
index 80e4300a..feb68c9d 100644
--- a/envs/uv-activitysim.sh
+++ b/envs/uv-activitysim.sh
@@ -96,7 +96,7 @@ cd -- "${TARGET_DIR}"
 eval "$(conda shell.bash hook)"
 
 # create a conda environment
-conda create -p .env/${TARGET_KERNEL} python=3.10 --yes
+conda create -p .env/${TARGET_KERNEL} python=3.11 --yes
 
 # activate the conda environment
 conda activate .env/${TARGET_KERNEL}
diff --git a/envs/windows.yaml b/envs/windows.yaml
index dd57bd27..7429d6a8 100644
--- a/envs/windows.yaml
+++ b/envs/windows.yaml
@@ -2,7 +2,7 @@ channels:
   - conda-forge
   - nodefaults
 dependencies:
-  - python=3.10
+  - python=3.11
   - pip
   - addicty
   - altair
diff --git a/pyproject.toml b/pyproject.toml
index 39a90915..5642d124 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,18 +26,19 @@ dependencies = [
     "xmle >=0.1.25",
     "rich",
     "sparse",
-    "scipy >=1.15,<1.16",
+    "scipy >=1.15",
     "Jinja2",
-    "sharrow >=2.15"
+    "sharrow >=2.16.2"
 ]
 readme = "README.md"
-requires-python = ">= 3.10"
+requires-python = ">= 3.11"
 
 [project.optional-dependencies]
 # development dependency groups
 test = [
     "pytest >=4.6",
-    "jax >=0.6,<0.7",
+    "ipython",
+    "jax >=0.6,<0.9",
     "tables",
     "geopandas",
     "matplotlib",
@@ -62,7 +63,12 @@ include = ["larch*"]
 namespaces = false
 
 [tool.setuptools.package-data]
-data_warehouse = ["*.csv.gz", "*.omx", "*.zarr.zip", "*.parquet"]
+larch = [
+    "data_warehouse/*.csv.gz",
+    "data_warehouse/*.omx",
+    "data_warehouse/*.zip",
+    "data_warehouse/*.parquet",
+]
 
 [tool.setuptools_scm]
 fallback_version = "6.0"
@@ -87,7 +93,7 @@ testpaths = [
 [tool.ruff]
 fix = true
 line-length = 88
-target-version = "py310"
+target-version = "py311"
 extend-include = ["*.ipynb"]
 extend-exclude = ["sandbox"]
 
@@ -129,7 +135,5 @@ ignore-overlong-task-comments = true
 convention = "numpy"
 
 [tool.uv.sources]
-sharrow = { workspace = true }
-
-[tool.uv.workspace]
-members = ["subs/*"]
+# Local development uses the same Python 3.14 fixes as the CI dependency wheel.
+sharrow = { path = "../sharrow", editable = true, marker = "python_version >= '3.14'" }
diff --git a/src/larch/model/constraints.py b/src/larch/model/constraints.py
index f88025a3..4d0019d4 100644
--- a/src/larch/model/constraints.py
+++ b/src/larch/model/constraints.py
@@ -116,9 +116,13 @@ def link_model(self, model, scale=None):
                 self.cmax_den = 0
 
     def _min_fun(self, x):
+        if isinstance(x, pd.Series):
+            x = x.to_numpy()
         return x[self.i_num] * self.cmin_num + x[self.i_den] * self.cmin_den
 
     def _max_fun(self, x):
+        if isinstance(x, pd.Series):
+            x = x.to_numpy()
         return x[self.i_num] * self.cmax_num + x[self.i_den] * self.cmax_den
 
     def fun(self, x):
@@ -271,6 +275,8 @@ def link_model(self, model, scale=None):
             self.scale = scale
 
     def fun(self, x):
+        if isinstance(x, pd.Series):
+            x = x.to_numpy()
         return (x[self.i_more] - x[self.i_less]) * self.scale
 
     def jac(self, x):
diff --git a/src/larch/model/gradient_check.py b/src/larch/model/gradient_check.py
index c346fb9a..e64295cf 100644
--- a/src/larch/model/gradient_check.py
+++ b/src/larch/model/gradient_check.py
@@ -260,7 +260,10 @@ def check_gradient(
     if not stylize:
         return f
     try:
-        return f.style.applymap(_color_poor_similarity, subset="similarity")
+        styler = f.style
+        if hasattr(styler, "map"):
+            return styler.map(_color_poor_similarity, subset="similarity")
+        return styler.applymap(_color_poor_similarity, subset="similarity")
     except ValueError:
         return f
 
diff --git a/src/larch/model/numbamodel.py b/src/larch/model/numbamodel.py
index db8eda88..5f4a7b11 100755
--- a/src/larch/model/numbamodel.py
+++ b/src/larch/model/numbamodel.py
@@ -2992,8 +2992,11 @@ def bold_if_signif(value):
                     {"selector": "td", "props": [("border", "1px solid #e5e5e5")]},
                 ]
             )
-            .applymap(bold_if_signif, subset=["signif"])
         )
+        if hasattr(output, "map"):
+            output = output.map(bold_if_signif, subset=["signif"])
+        else:
+            output = output.applymap(bold_if_signif, subset=["signif"])
         if caption:
             output.set_caption(caption).set_table_styles(
                 [
diff --git a/src/larch/model/optimization.py b/src/larch/model/optimization.py
index a036490f..ab539486 100644
--- a/src/larch/model/optimization.py
+++ b/src/larch/model/optimization.py
@@ -349,11 +349,14 @@ def callback(x, status=None):
 
                 # args = getattr(model, "_null_slice", (0, -1, 1))
                 raw_result = minimize(
-                    model.logloss,
-                    model.pvals,
+                    lambda params, *args: float(model.logloss(params, *args)),
+                    np.asarray(model.pvals, dtype=np.float64),
                     # args=args,
                     method=method,
-                    jac=model.d_logloss,
+                    # SLSQP requires float64 even when a JAX model uses float32.
+                    jac=lambda params, *args: np.asarray(
+                        model.d_logloss(params, *args), dtype=np.float64
+                    ),
                     bounds=bounds,
                     callback=callback,
                     options=options,
diff --git a/src/larch/optimize.py b/src/larch/optimize.py
index 2270940d..56192e73 100644
--- a/src/larch/optimize.py
+++ b/src/larch/optimize.py
@@ -148,7 +148,7 @@ def __neg_loglike(self, params):
                     params=self.__rich_table(params),
                     bestloglike=self._cached_loglike_best,
                 )
-        return result
+        return float(result)
 
     def jax_neg_d_loglike(self, *args, **kwargs):
         return -self.jax_d_loglike(*args, **kwargs)
@@ -157,7 +157,9 @@ def __neg_d_loglike(self, *args, **kwargs):
         result = self.jax_neg_d_loglike(*args, **kwargs)
         result = result.at[self.pholdfast != 0].set(0)  # zero out holdfast parameters
         self._latest_gradient = -result
-        return result
+        # SciPy's SLSQP implementation requires double precision inputs,
+        # including when JAX computes the gradient in single precision.
+        return np.asarray(result, dtype=np.float64)
 
     def _check_if_best(self, computed_ll, pvalues=None):
         if np.isnan(computed_ll):
@@ -229,7 +231,7 @@ def jax_maximize_loglike(
             self.dashboard.update_content(status="[yellow]optimizing parameters...")
             result = minimize(
                 self.__neg_loglike,
-                self.pvals,
+                np.asarray(self.pvals, dtype=np.float64),
                 jac=self.__neg_d_loglike,
                 method=method,
                 **kwargs,
diff --git a/tests/test_vehicle.py b/tests/test_vehicle.py
index 69994620..a1c25a01 100644
--- a/tests/test_vehicle.py
+++ b/tests/test_vehicle.py
@@ -3,6 +3,8 @@
 import jax  # noqa: F401
 import numpy as np
 import pandas as pd
+import scipy
+from packaging.version import Version
 from pytest import approx, fixture
 
 import larch as lx
@@ -113,9 +115,12 @@ def test_mixed_logit(simple_model: lx.Model):
         rtol=1e-5,
     )
 
-    result0 = mixed.maximize_loglike(stderr=True, options={"maxiter": 2})
+    # SciPy 1.16's SLSQP rewrite counts the first update toward maxiter.
+    # Keep this checkpoint at one update on both implementations.
+    maxiter = 2 if Version(scipy.__version__) < Version("1.16") else 1
+    result0 = mixed.maximize_loglike(stderr=True, options={"maxiter": maxiter})
     assert not result0["success"]
-    assert result0["nit"] == 2
+    assert result0["nit"] == maxiter
     assert result0["loglike"] == approx(-1565.2197265625)
     expected_value = {
         "ev": -0.14029760159652338,
diff --git a/uv.lock b/uv.lock
index 53fffabb..8ca7ebe3 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,17 +1,11 @@
 version = 1
 revision = 3
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.13'",
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
     "python_full_version == '3.12.*'",
-    "python_full_version == '3.11.*'",
-    "python_full_version < '3.11'",
-]
-
-[manifest]
-members = [
-    "larch",
-    "sharrow",
+    "python_full_version < '3.12'",
 ]
 
 [[package]]
@@ -52,12 +46,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
 ]
 
-[[package]]
-name = "asciitree"
-version = "0.3.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/2d/6a/885bc91484e1aa8f618f6f0228d76d0e67000b0fdd6090673b777e311913/asciitree-0.3.3.tar.gz", hash = "sha256:4aa4b9b649f85e3fcb343363d97564aa1fb62e249677f2e18a96765145cc0f6e", size = 3951, upload-time = "2016-09-05T19:10:42.681Z" }
-
 [[package]]
 name = "asttokens"
 version = "3.0.0"
@@ -84,19 +72,13 @@ dependencies = [
     { name = "msgpack" },
     { name = "ndindex" },
     { name = "numexpr", marker = "platform_machine != 'wasm32'" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "platformdirs" },
     { name = "py-cpuinfo", marker = "platform_machine != 'wasm32'" },
     { name = "requests" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9d/e7/18ec906efbbb4a0aada836694a79ffb2cd0ceb476e553d4d84f0cce877c5/blosc2-3.9.1.tar.gz", hash = "sha256:3808a39002da38a381c933ec5d6c30ef05062f47693ee3f0ab9a2f447c612f0a", size = 3831628, upload-time = "2025-10-01T06:39:31.073Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b5/29/7aceed403c4152df9d3503af94938f0989fb70592aff81aac3caec19ea82/blosc2-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:519c7d4a6f6fc7932fc6523121099ff4edcdfc8d81035e06f4d72d1d41b4df77", size = 3951571, upload-time = "2025-10-01T06:38:42.71Z" },
-    { url = "https://files.pythonhosted.org/packages/81/cd/e51fa31ff7c53fa509ece3a3c4a11c9b860c372ae925ffc4175613be2d10/blosc2-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5dc4440b9912850ba5bc3b432cd011d379bd1a51ad913839169fec132e319399", size = 3453554, upload-time = "2025-10-01T06:38:45.326Z" },
-    { url = "https://files.pythonhosted.org/packages/26/9e/a0d837a8d349546af7295a37856894242ca77f335ae783e71fe459cff6c2/blosc2-3.9.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3a6453c742c4e51c019b71bd2a28cc29e8d99805d17cc6c4b59b06759633faa7", size = 4374148, upload-time = "2025-10-01T06:38:47.105Z" },
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+    { name = "ipython" },
+    { name = "jax", version = "0.6.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "jax", version = "0.8.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
     { name = "matplotlib" },
     { name = "nbmake" },
     { name = "openmatrix" },
@@ -1150,13 +1109,11 @@ test = [
     { name = "pytest" },
     { name = "pytest-regressions" },
     { name = "seaborn" },
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-    { name = "tables", version = "3.10.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "tables" },
     { name = "tabulate" },
     { name = "vl-convert-python" },
     { name = "xlsxwriter" },
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-    { name = "zarr", version = "3.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "zarr" },
 ]
 
 [package.metadata]
@@ -1166,8 +1123,9 @@ requires-dist = [
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     { name = "filelock" },
     { name = "geopandas", marker = "extra == 'test'" },
+    { name = "ipython", marker = "extra == 'test'" },
     { name = "jax", marker = "extra == 'jax'" },
-    { name = "jax", marker = "extra == 'test'", specifier = ">=0.6,<0.7" },
+    { name = "jax", marker = "extra == 'test'", specifier = ">=0.6,<0.9" },
     { name = "jinja2" },
     { name = "matplotlib", marker = "extra == 'test'" },
     { name = "nbmake", marker = "extra == 'test'" },
@@ -1182,9 +1140,10 @@ requires-dist = [
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     { name = "pytest-regressions", marker = "extra == 'test'" },
     { name = "rich" },
-    { name = "scipy", specifier = ">=1.15,<1.16" },
+    { name = "scipy", specifier = ">=1.15" },
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-    { name = "sharrow", editable = "subs/sharrow" },
+    { name = "sharrow", marker = "python_full_version < '3.14'", specifier = ">=2.16.2" },
+    { name = "sharrow", marker = "python_full_version >= '3.14'", editable = "../sharrow" },
     { name = "sparse" },
     { name = "tables", marker = "extra == 'test'" },
     { name = "tabulate", marker = "extra == 'test'" },
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 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version == '3.12.*'",
-    "python_full_version == '3.11.*'",
-]
 dependencies = [
-    { name = "donfig", marker = "python_full_version >= '3.11'" },
-    { name = "numcodecs", version = "0.16.3", source = { registry = "https://pypi.org/simple" }, extra = ["crc32c"], marker = "python_full_version >= '3.11'" },
-    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "packaging", marker = "python_full_version >= '3.11'" },
-    { name = "typing-extensions", marker = "python_full_version >= '3.11'" },
+    { name = "donfig" },
+    { name = "numcodecs", version = "0.16.5", source = { registry = "https://pypi.org/simple" }, extra = ["crc32c"], marker = "python_full_version < '3.12'" },
+    { name = "numcodecs", version = "0.17.0", source = { registry = "https://pypi.org/simple" }, extra = ["crc32c"], marker = "python_full_version >= '3.12'" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/d6/67/14be68a7bad15eecda09b1e81fca2420f7533645fe187bf4d6104c1aad52/zarr-3.1.3.tar.gz", hash = "sha256:01342f3e26a02ed5670db608a5576fbdb8d76acb5c280bd2d0082454b1ba6f79", size = 349125, upload-time = "2025-09-18T19:32:41.688Z" }
 wheels = [

From 9df263e81d2c551cfc35e387243234718cdea808 Mon Sep 17 00:00:00 2001
From: Jan Zill <jan@outerloop.io>
Date: Wed, 14 Oct 2026 22:18:24 +1000
Subject: [PATCH 2/4] jax constraints fix, altair dep update

---
 src/larch/model/numbamodel.py |  12 +-
 src/larch/optimize.py         |  83 +++++++++++---
 tests/test_constraints.py     | 210 ++++++++++++++++++++++++++++++++++
 tests/test_mtc.py             |  13 ++-
 uv.lock                       |   8 +-
 5 files changed, 301 insertions(+), 25 deletions(-)
 create mode 100644 tests/test_constraints.py

diff --git a/src/larch/model/numbamodel.py b/src/larch/model/numbamodel.py
index 5f4a7b11..cf6bd61a 100755
--- a/src/larch/model/numbamodel.py
+++ b/src/larch/model/numbamodel.py
@@ -29,7 +29,7 @@
 from .numba_stream import ModelStreamer
 
 if TYPE_CHECKING:
-    import altair.vegalite.v5.api
+    import altair
 
 if not list(Path(__file__).parent.glob("__pycache__/numbamodel.*.nbc")):
     warnings.warn(  ## Good news, everyone! ##  )
@@ -2138,13 +2138,17 @@ def logloss(
         )
         return -result / self.total_weight()
 
-    def neg_d_loglike(self, x=None, start_case=0, stop_case=-1, step_case=1, **kwargs):
+    def neg_d_loglike(
+        self, x=None, start_case=None, stop_case=None, step_case=None, **kwargs
+    ):
         result = self.d_loglike(
             x, start_case=start_case, stop_case=stop_case, step_case=step_case, **kwargs
         )
         return -np.asarray(result)
 
-    def d_logloss(self, x=None, start_case=0, stop_case=-1, step_case=1, **kwargs):
+    def d_logloss(
+        self, x=None, start_case=None, stop_case=None, step_case=None, **kwargs
+    ):
         result = self.d_loglike(
             x, start_case=start_case, stop_case=stop_case, step_case=step_case, **kwargs
         )
@@ -3028,7 +3032,7 @@ def analyze_predictions_co_figure(
         width: int = 400,
         alt_labels: Literal["id", "name"] = "name",
         bins=None,
-    ) -> altair.vegalite.v5.api.FacetChart:
+    ) -> altair.FacetChart:
         """
         Create an Altair figure of the model's predictions based on idco attributes.
 
diff --git a/src/larch/optimize.py b/src/larch/optimize.py
index 56192e73..2da4996e 100644
--- a/src/larch/optimize.py
+++ b/src/larch/optimize.py
@@ -6,7 +6,7 @@
 
 import numpy as np
 from rich.table import Table
-from scipy.optimize import Bounds, minimize
+from scipy.optimize import Bounds, LinearConstraint, NonlinearConstraint, minimize
 
 from ._optional import jax, jnp
 from .compiled import jitmethod
@@ -214,11 +214,41 @@ def jax_maximize_loglike(
             throttle=dashboard_update if dashboard else 9999,
         )
         try:
+            model_constraints = getattr(self, "constraints", ())
+            if model_constraints:
+                if method.lower() in {"slsqp", "cobyla"}:
+                    constraints = [
+                        constraint
+                        for model_constraint in model_constraints
+                        for constraint in model_constraint.as_constraint_dicts()
+                    ]
+                elif method.lower() == "trust-constr":
+                    constraints = [
+                        constraint
+                        for model_constraint in model_constraints
+                        for constraint in model_constraint.as_linear_constraints()
+                    ]
+                else:
+                    raise ValueError(
+                        f"{method} does not support model constraints; "
+                        "use SLSQP, COBYLA, or trust-constr"
+                    )
+                supplied_constraints = kwargs.get("constraints")
+                if supplied_constraints is not None:
+                    if isinstance(
+                        supplied_constraints,
+                        (dict, LinearConstraint, NonlinearConstraint),
+                    ):
+                        constraints.append(supplied_constraints)
+                    else:
+                        constraints.extend(supplied_constraints)
+                kwargs["constraints"] = constraints
             if method.lower() in {
                 "nelder-mead",
                 "l-bfgs-b",
                 "tnc",
                 "slsqp",
+                "cobyla",
                 "powell",
                 "trust-constr",
             }:
@@ -229,13 +259,41 @@ def jax_maximize_loglike(
             )
             self.__neg_d_loglike(self.pvals)
             self.dashboard.update_content(status="[yellow]optimizing parameters...")
+            objective = self.__neg_loglike
+            gradient = self.__neg_d_loglike
+            objective_scale = 1
+            optimizer_constraints = kwargs.get("constraints")
+            if isinstance(
+                optimizer_constraints, (dict, LinearConstraint, NonlinearConstraint)
+            ) or optimizer_constraints:
+                # Precondition constrained fits using mean logloss, as Numba
+                # does. Large total gradients can prevent SLSQP from reaching
+                # feasibility; leave unconstrained optimizer trajectories intact.
+                objective_scale = (
+                    self.total_weight() if hasattr(self, "total_weight") else 1
+                )
+                objective = lambda params: self.__neg_loglike(params) / objective_scale
+                gradient = lambda params: self.__neg_d_loglike(params) / objective_scale
             result = minimize(
-                self.__neg_loglike,
+                objective,
                 np.asarray(self.pvals, dtype=np.float64),
-                jac=self.__neg_d_loglike,
+                jac=gradient if method.lower() != "cobyla" else None,
                 method=method,
                 **kwargs,
             )
+            # Restore total units for likelihood derivatives and multipliers.
+            # Solver-specific inverse Hessian approximations remain on the
+            # normalized optimizer scale.
+            if objective_scale != 1:
+                if "fun" in result:
+                    result["fun"] *= objective_scale
+                if "jac" in result and method.lower() != "trust-constr":
+                    result["jac"] *= objective_scale
+                for key in ("grad", "lagrangian_grad", "multipliers"):
+                    if key in result:
+                        result[key] *= objective_scale
+                if "v" in result:
+                    result["v"] = [values * objective_scale for values in result["v"]]
 
             try:
                 result["n_cases"] = self.n_cases
@@ -249,7 +307,9 @@ def jax_maximize_loglike(
                     result["logloss"] = result["fun"] / self.total_weight()
                 result["loglike"] = -result["fun"]
                 del result["fun"]
-            if "jac" in result:
+            # trust-constr's jac describes the constraints; its grad retains
+            # SciPy's minimized-objective sign. Other jac results describe LL.
+            if "jac" in result and method.lower() != "trust-constr":
                 result["jac"] = -result["jac"]
             self.dashboard.update_content(
                 loglike=result["loglike"],
@@ -260,16 +320,11 @@ def jax_maximize_loglike(
                 self.dashboard.update_content(
                     status="[yellow]computing standard errors..."
                 )
-                se, hess, ihess = self.jax_param_cov(result.x)
-                result["stderr"] = np.asarray(se)
-                hess = np.asarray(hess).copy()
-                hess[self.pholdfast.astype(bool), :] = 0
-                hess[:, self.pholdfast.astype(bool)] = 0
-                ihess = np.asarray(ihess).copy()
-                ihess[self.pholdfast.astype(bool), :] = 0
-                ihess[:, self.pholdfast.astype(bool)] = 0
-                self.add_parameter_array("hess", hess)
-                self.add_parameter_array("ihess", ihess)
+                self.calculate_parameter_covariance(result.x)
+                # The covariance calculation projects binding constraints into
+                # pstderr while retaining the unprojected inverse Hessian.
+                se = np.asarray(self.pstderr).copy()
+                result["stderr"] = se
                 self.dashboard.update_content(
                     params=self.__rich_table(result.x, se),
                 )
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
new file mode 100644
index 00000000..3562266b
--- /dev/null
+++ b/tests/test_constraints.py
@@ -0,0 +1,210 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from pytest import approx
+from scipy.optimize import LinearConstraint, NonlinearConstraint
+
+import larch as lx
+from larch.model.constraints import OrderingBound
+
+
+def _choice_model(compute_engine, counts=(10, 30, 60), *, shared=False):
+    choices = np.repeat(np.arange(1, len(counts) + 1), counts)
+    frame = pd.DataFrame({"choice": choices}).rename_axis("caseid")
+    dataset = lx.Dataset.construct.from_idco(frame, alts=np.arange(1, len(counts) + 1))
+    model = lx.Model(dataset, compute_engine=compute_engine)
+    model.choice_co_code = "choice"
+    for alt in range(1, len(counts)):
+        model.utility_co[alt] = lx.P("shared" if shared else f"asc_{alt}")
+    model.utility_co[len(counts)] = 0
+    model.set_cap(5)
+    return model
+
+
+def _estimate(model, **kwargs):
+    return model.maximize_loglike(
+        method="slsqp", stderr=True, quiet=True, options={"ftol": 1e-8}, **kwargs
+    )
+
+
+def test_ordering_constraint_estimation_and_covariance_parity():
+    # The observed shares prefer ASC_2 > ASC_1; the constraint binds them together.
+    models = [_choice_model(engine) for engine in ("numba", "jax")]
+    explicit = _choice_model("numba", shared=True)
+    explicit_result = _estimate(explicit)
+    expected_value = np.log(40 / (2 * 60))
+    expected_covariance = np.full((2, 2), 1 / (100 * 0.4 * 0.6))
+    for model in models:
+        model.constraints = [OrderingBound("asc_2", "asc_1")]
+        result = _estimate(model)
+        assert result.success
+        assert result.loglike == approx(explicit_result.loglike, abs=2e-4)
+        np.testing.assert_allclose(model.pvals, expected_value, atol=2e-4)
+        covariance = model.parameters["covariance_matrix"].values
+        np.testing.assert_allclose(covariance, expected_covariance, rtol=2e-4)
+        np.testing.assert_allclose(model.pstderr, np.sqrt(covariance.diagonal()))
+        assert (model.parameters["constrained"].values != "").all()
+        if model.compute_engine == "jax":
+            np.testing.assert_allclose(result.stderr, model.pstderr)
+            np.testing.assert_allclose(result.stderr, np.sqrt(1 / 24), rtol=2e-4)
+
+        # Evaluate both Hessians and projections at exactly the same optimum.
+        model.pvals = np.full(2, expected_value)
+        model.calculate_parameter_covariance()
+
+    np.testing.assert_allclose(
+        models[0].parameters["covariance_matrix"],
+        models[1].parameters["covariance_matrix"],
+        rtol=2e-5,
+    )
+    np.testing.assert_allclose(
+        expected_covariance,
+        np.full((2, 2), explicit.pstderr[0] ** 2),
+        rtol=2e-5,
+    )
+
+
+@pytest.mark.parametrize("compute_engine", ["numba", "jax"])
+def test_active_parameter_bound_and_holdfast_covariance(compute_engine):
+    model = _choice_model(compute_engine, counts=(10, 30, 20, 40))
+    model.lock_value(asc_1=-2)
+    model.pmaximum = {"asc_2": -1}
+    result = _estimate(model)
+    assert result.success
+    assert model.get_value("asc_1") == -2
+    assert model.get_value("asc_2") == approx(-1, abs=1e-6)
+    assert model.get_value("asc_3") == approx(
+        np.log((0.2 / 0.8) * (1 + np.exp(-2) + np.exp(-1))), abs=2e-4
+    )
+    covariance = model.parameters["covariance_matrix"].values
+    np.testing.assert_allclose(covariance, np.diag([0, 0, 1 / 16]), atol=1e-6)
+    np.testing.assert_allclose(model.pstderr, [0, 0, 0.25], atol=1e-5)
+    assert model.parameters["constrained"].sel(param_name="asc_1").item() == "fixed value"
+    assert model.parameters["constrained"].sel(param_name="asc_2").item()
+    if compute_engine == "jax":
+        np.testing.assert_allclose(result.stderr, model.pstderr)
+        np.testing.assert_allclose(result.stderr, [0, 0, 0.25], atol=1e-5)
+
+
+@pytest.mark.parametrize(
+    "case_slice",
+    [slice(None), slice(5, 90, 2), slice(None, -1)],
+    ids=["all", "slice", "exclude-last"],
+)
+def test_numba_objective_gradients_match_observations_and_finite_differences(case_slice):
+    model = _choice_model("numba")
+    # Equal initial probabilities give gradients directly from observed counts.
+    choices = np.repeat([1, 2, 3], [10, 30, 60])[case_slice]
+    expected_gradient = np.bincount(choices, minlength=4)[1:3] - len(choices) / 3
+    slice_kwargs = (
+        {}
+        if case_slice == slice(None)
+        else {
+            "start_case": case_slice.start,
+            "stop_case": case_slice.stop,
+            "step_case": case_slice.step,
+        }
+    )
+    np.testing.assert_allclose(model.neg_d_loglike(**slice_kwargs), -expected_gradient)
+    np.testing.assert_allclose(model.d_logloss(**slice_kwargs), -expected_gradient / 100)
+
+    params = np.asarray([-0.2, 0.3])
+    epsilon = 1e-6
+    for objective, gradient in [
+        (model.neg_loglike, model.neg_d_loglike),
+        (model.logloss, model.d_logloss),
+    ]:
+        finite_difference = []
+        for displacement in np.eye(model.n_params) * epsilon:
+            finite_difference.append(
+                (
+                    objective(params + displacement, **slice_kwargs)
+                    - objective(params - displacement, **slice_kwargs)
+                )
+                / (2 * epsilon)
+            )
+        np.testing.assert_allclose(
+            gradient(params, **slice_kwargs), finite_difference, rtol=1e-6, atol=1e-7
+        )
+
+
+@pytest.mark.parametrize("constraint_type", ["dict", "linear", "nonlinear", "sequence"])
+def test_jax_preserves_caller_constraints_alongside_model_constraints(constraint_type):
+    model = _choice_model("jax")
+    model.constraints = [OrderingBound("asc_2", "asc_1")]
+    a = model.get_param_loc("asc_1")
+    direction = np.eye(model.n_params)[a]
+    if constraint_type == "dict":
+        constraint = {
+            "type": "ineq",
+            "fun": lambda params: -1.5 - params[a],
+            "jac": lambda params: -direction,
+        }
+    elif constraint_type == "nonlinear":
+        constraint = NonlinearConstraint(
+            lambda params: params[a], -np.inf, -1.5, jac=lambda params: direction
+        )
+    else:
+        constraint = LinearConstraint(direction, -np.inf, -1.5)
+        if constraint_type == "sequence":
+            constraint = [constraint]
+    result = model.maximize_loglike(
+        method="slsqp", quiet=True, constraints=constraint, options={"ftol": 1e-8}
+    )
+    assert result.success
+    # Dropping either constraint gives a different, less restrictive optimum.
+    np.testing.assert_allclose(model.pvals, [-1.5, -1.5], atol=2e-4)
+
+
+def test_jax_rejects_optimizer_that_cannot_apply_model_constraints():
+    model = _choice_model("jax")
+    model.constraints = [OrderingBound("asc_2", "asc_1")]
+    with pytest.raises(ValueError, match="constraint"):
+        model.maximize_loglike(method="l-bfgs-b", quiet=True)
+
+
+def test_jax_unconstrained_fit_with_readonly_double_precision_gradient():
+    jax = pytest.importorskip("jax")
+    with jax.experimental.enable_x64():
+        model = _choice_model("jax")
+        result = model.maximize_loglike(
+            method="slsqp", quiet=True, options={"ftol": 1e-10}
+        )
+        assert result.success
+        np.testing.assert_allclose(
+            model.pvals, np.log(np.asarray([10, 30]) / 60), atol=2e-5
+        )
+        expected_loglike = np.asarray([10, 30, 60]) @ np.log([0.1, 0.3, 0.6])
+        assert result.loglike == approx(expected_loglike, abs=1e-8)
+        assert result.jac.dtype == np.float64
+        np.testing.assert_allclose(result.jac, model.d_loglike(), atol=1e-8)
+
+
+@pytest.mark.parametrize("method", ["trust-constr", "cobyla"])
+def test_jax_constrained_solver_applies_parameter_bounds_and_preserves_metadata(method):
+    model = _choice_model("jax")
+    model.constraints = [OrderingBound("asc_2", "asc_1")]
+    model.pmaximum = {"asc_1": -1.5}
+    if method == "trust-constr":
+        options = {
+            "gtol": 1e-8,
+            "barrier_tol": 1e-9,
+            "initial_barrier_parameter": 1e-3,
+            "maxiter": 300,
+        }
+    else:
+        options = {"tol": 1e-5, "catol": 1e-6, "maxiter": 300}
+    result = model.maximize_loglike(method=method, quiet=True, options=options)
+    assert result.success
+    assert model.get_value("asc_1") <= -1.5 + 1e-6
+    assert model.get_value("asc_2") <= model.get_value("asc_1") + 1e-6
+    np.testing.assert_allclose(model.pvals, [-1.5, -1.5], atol=2e-3)
+    assert np.isfinite(result.loglike)
+    if method == "trust-constr":
+        assert isinstance(result.jac, list)
+        assert all(np.isfinite(jacobian).all() for jacobian in result.jac)
+        assert all(np.isfinite(multiplier).all() for multiplier in result.v)
+        assert np.isfinite(result.lagrangian_grad).all()
+        np.testing.assert_allclose(result.grad, -model.d_loglike(), atol=2e-5)
diff --git a/tests/test_mtc.py b/tests/test_mtc.py
index bc10ee12..e2c091c1 100644
--- a/tests/test_mtc.py
+++ b/tests/test_mtc.py
@@ -315,7 +315,7 @@ def test_mtc_22(compute_engine):
     )
 
 
-@pytest.mark.parametrize("compute_engine", ["numba"])
+@pytest.mark.parametrize("compute_engine", ["numba", "jax"])
 def test_mtc_30(compute_engine):
     m = lx.example(1)
     m.title = "MTC Example 30 (Constrained Simple MNL)"
@@ -326,7 +326,7 @@ def test_mtc_30(compute_engine):
     result_explicit = m_explicit.maximize_loglike(
         stderr=True, options={"maxiter": 1000, "ftol": 1e-10}
     )
-    revealed_x = dict(zip(m.pnames, result_explicit.x))
+    revealed_x = dict(zip(m_explicit.pnames, result_explicit.x))
     print(revealed_x)
     assert result_explicit.logloss == approx(0.7533452255366115)
     assert result_explicit.loglike == approx(-3788.5731392236194)
@@ -342,7 +342,7 @@ def test_mtc_30(compute_engine):
         "hhinc#4": -0.006153651399668838,
         "hhinc#5": -0.014112829960368308,
         "hhinc#6": -0.009343128298864946,
-        "totcost": -0.0017390713018709733,
+        "tottime": -0.0017390713018709733,
     }
     revealed_x = {k: v for k, v in revealed_x.items() if k in expected_x}
     assert revealed_x == approx(expected_x, rel=0.1)
@@ -366,6 +366,13 @@ def test_mtc_30(compute_engine):
     assert result.logloss == approx(0.753345269140234)
     assert result.message == "Optimization terminated successfully"
     assert m.total_weight() == 5029.0
+    assert m.get_value("totcost") / m.get_value("tottime") == approx(3.0, abs=1e-5)
+    covariance = m.parameters["covariance_matrix"].values
+    np.testing.assert_allclose(m.pstderr, np.sqrt(covariance.diagonal()))
+    constraint_gradient = m.constraints[0].jac(m.pvals)
+    assert constraint_gradient @ covariance @ constraint_gradient == approx(0, abs=1e-7)
+    if compute_engine == "jax":
+        np.testing.assert_allclose(result.stderr, m.pstderr)
     m.parameter_summary()
     _summary = m.parameter_summary()
     # assert_same_text(
diff --git a/uv.lock b/uv.lock
index 8ca7ebe3..6cbc5abe 100644
--- a/uv.lock
+++ b/uv.lock
@@ -23,18 +23,18 @@ wheels = [
 
 [[package]]
 name = "altair"
-version = "5.5.0"
+version = "6.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jinja2" },
     { name = "jsonschema" },
     { name = "narwhals" },
     { name = "packaging" },
-    { name = "typing-extensions", marker = "python_full_version < '3.14'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/16/b1/f2969c7bdb8ad8bbdda031687defdce2c19afba2aa2c8e1d2a17f78376d8/altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d", size = 705305, upload-time = "2024-11-23T23:39:58.542Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e8/de/9731304fae332b5783fdc3ed18531dfaf4a3cc62a66aaaa74c047355b70f/altair-6.3.0.tar.gz", hash = "sha256:fa632121aca6d0fcb198a3d8a9f7d937fa5b7518ec947951cfffb44e736d97c8", size = 766362, upload-time = "2026-09-15T06:38:03.623Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/aa/f3/0b6ced594e51cc95d8c1fc1640d3623770d01e4969d29c0bd09945fafefa/altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c", size = 731200, upload-time = "2024-11-23T23:39:56.4Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/b9/10a8bb13a0462e0bc4e02c0b7b8237dc24ab590f5bb4be94bec5e8f1d532/altair-6.3.0-py3-none-any.whl", hash = "sha256:7defb6ca730676dfc99a299768e2769f51585fcb3dc960ea71aacc368929d65e", size = 797192, upload-time = "2026-09-15T06:38:01.791Z" },
 ]
 
 [[package]]

From 6844c0bedc57e56bcafec0d1a5d2d68446a9e4a2 Mon Sep 17 00:00:00 2001
From: Jan Zill <jan@outerloop.io>
Date: Thu, 15 Oct 2026 10:14:11 +1000
Subject: [PATCH 3/4] changes to for full test suite, example precision,
 graphviz dev install, deps update

---
 .../exampville/204_exville_dest_only.ipynb    |  60 +--
 docs/examples/mtc/007_ovtt_by_distance.ipynb  |  28 +-
 docs/examples/mtc/009_ttr_4.ipynb             |   2 +-
 docs/examples/mtc/017A_market_seg.ipynb       |  54 +--
 docs/examples/mtc/017B_alt_segmentation.ipynb |  54 +--
 .../mtc/017C_segmentation_males.ipynb         |  54 +--
 .../mtc/017D_segmentation_female.ipynb        |  54 +--
 docs/examples/mtc/017_mnl.ipynb               |  54 +--
 docs/examples/mtc/030_mnl_constrain.ipynb     |  31 +-
 .../151-swissmetro-panel-latent-class.ipynb   |  34 +-
 .../xlogit-artificial/xlogit-test.ipynb       | 416 +++++++++---------
 envs/install-graphviz.sh                      |  71 +++
 envs/uv-dev.sh                                |   5 +
 exercises/mixed-logit.ipynb                   |   5 +-
 pyproject.toml                                |   3 +-
 uv.lock                                       |  16 +-
 16 files changed, 501 insertions(+), 440 deletions(-)
 create mode 100755 envs/install-graphviz.sh

diff --git a/docs/examples/exampville/204_exville_dest_only.ipynb b/docs/examples/exampville/204_exville_dest_only.ipynb
index 0d481ffb..2caa5e33 100644
--- a/docs/examples/exampville/204_exville_dest_only.ipynb
+++ b/docs/examples/exampville/204_exville_dest_only.ipynb
@@ -980,7 +980,7 @@
     "    ).sort_index(),\n",
     "    rtol=1e-2,\n",
     ")\n",
-    "assert m.pstderr == approx(\n",
+    "assert mg.pstderr == approx(\n",
     "    np.array([0.145749, 0.052355, 0.0, 0.0, 0.009012, 0.003812]),\n",
     "    rel=1e-3,\n",
     ")"
@@ -1012,61 +1012,9 @@
    "outputs": [],
    "source": [
     "# TEST\n",
-    "assert mg.bhhh() == approx(\n",
-    "    np.array(\n",
-    "        [\n",
-    "            [\n",
-    "                4.92276067e01,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                1.82962749e02,\n",
-    "                2.35657294e02,\n",
-    "            ],\n",
-    "            [\n",
-    "                0.00000000e00,\n",
-    "                3.66140769e02,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                -2.02327809e02,\n",
-    "                5.64802080e02,\n",
-    "            ],\n",
-    "            [\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "            ],\n",
-    "            [\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "            ],\n",
-    "            [\n",
-    "                1.82962749e02,\n",
-    "                -2.02327809e02,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                1.45027493e04,\n",
-    "                1.20949516e04,\n",
-    "            ],\n",
-    "            [\n",
-    "                2.35657294e02,\n",
-    "                5.64802080e02,\n",
-    "                0.00000000e00,\n",
-    "                0.00000000e00,\n",
-    "                1.20949516e04,\n",
-    "                8.59976140e04,\n",
-    "            ],\n",
-    "        ]\n",
-    "    ),\n",
-    "    rel=1e-2,\n",
-    ")"
+    "# Aggregation preserves alternative-level scores. Evaluate both models at\n",
+    "# the same coefficients so optimizer stopping tolerances do not affect parity.\n",
+    "assert mg.bhhh() == approx(m.bhhh(mg.pvals), rel=1e-8, abs=1e-8)"
    ]
   }
  ],
diff --git a/docs/examples/mtc/007_ovtt_by_distance.ipynb b/docs/examples/mtc/007_ovtt_by_distance.ipynb
index f3e95c92..99739350 100644
--- a/docs/examples/mtc/007_ovtt_by_distance.ipynb
+++ b/docs/examples/mtc/007_ovtt_by_distance.ipynb
@@ -214,7 +214,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True)\n",
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})\n",
     "m.calculate_parameter_covariance()\n",
     "m.loglike()"
    ]
@@ -312,19 +312,19 @@
     "from pytest import approx\n",
     "\n",
     "expected_x = {\n",
-    "    \"ASC_BIKE\": -2.675340279013382,\n",
-    "    \"ASC_SR2\": -2.1871708718901455,\n",
-    "    \"ASC_SR3P\": -3.5170483581773535,\n",
-    "    \"ASC_TRAN\": -0.039611848334672385,\n",
-    "    \"ASC_WALK\": -1.0206078711851112,\n",
-    "    \"hhinc#2,3\": -0.001383608162986253,\n",
-    "    \"hhinc#4\": -0.007257729971526785,\n",
-    "    \"hhinc#5\": -0.01196363695212793,\n",
-    "    \"hhinc#6\": -0.008182417861122782,\n",
-    "    \"motorized_ovtbydist\": -0.18145744778558676,\n",
-    "    \"motorized_time\": -0.04156509908994426,\n",
-    "    \"nonmotorized_time\": -0.04755705411055601,\n",
-    "    \"totcost\": -0.004114472584806616,\n",
+    "    \"ASC_BIKE\": -2.6867134884041124,\n",
+    "    \"ASC_SR2\": -2.188408263476616,\n",
+    "    \"ASC_SR3P\": -3.5180530612046663,\n",
+    "    \"ASC_TRAN\": -0.042203403709053865,\n",
+    "    \"ASC_WALK\": -1.0228699972401316,\n",
+    "    \"hhinc#2,3\": -0.0013767552257365773,\n",
+    "    \"hhinc#4\": -0.007247420724207541,\n",
+    "    \"hhinc#5\": -0.011858861031538045,\n",
+    "    \"hhinc#6\": -0.008237733460896757,\n",
+    "    \"motorized_ovtbydist\": -0.18121891400931894,\n",
+    "    \"motorized_time\": -0.04154068238646636,\n",
+    "    \"nonmotorized_time\": -0.04746566631597504,\n",
+    "    \"totcost\": -0.0041167524654853885,\n",
     "}\n",
     "for k in expected_x:\n",
     "    assert revealed_x[k] == approx(expected_x[k], 2e-2), (\n",
diff --git a/docs/examples/mtc/009_ttr_4.ipynb b/docs/examples/mtc/009_ttr_4.ipynb
index 4874a69d..b20149d7 100644
--- a/docs/examples/mtc/009_ttr_4.ipynb
+++ b/docs/examples/mtc/009_ttr_4.ipynb
@@ -211,7 +211,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True)\n",
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})\n",
     "m.calculate_parameter_covariance()\n",
     "m.loglike()"
    ]
diff --git a/docs/examples/mtc/017A_market_seg.ipynb b/docs/examples/mtc/017A_market_seg.ipynb
index 02bff184..5828c403 100644
--- a/docs/examples/mtc/017A_market_seg.ipynb
+++ b/docs/examples/mtc/017A_market_seg.ipynb
@@ -276,7 +276,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-10})\n",
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})\n",
     "m.calculate_parameter_covariance()\n",
     "m.loglike()"
    ]
@@ -380,32 +380,32 @@
     "from pytest import approx\n",
     "\n",
     "expected_x = {\n",
-    "    \"ASC_Bike\": 0.9742143708899291,\n",
-    "    \"ASC_SR2\": 0.5953670936985695,\n",
-    "    \"ASC_SR3+\": -0.7824827134037493,\n",
-    "    \"ASC_Transit\": 2.2585074587816134,\n",
-    "    \"ASC_Walk\": 2.9039591287055986,\n",
-    "    \"costbyincome\": -0.02264018916095553,\n",
-    "    \"hhinc#4\": -0.006444094209484317,\n",
-    "    \"hhinc#5\": -0.011725009048022181,\n",
-    "    \"hhinc#6\": -0.01198956540981421,\n",
-    "    \"motorized_ovtbydist\": -0.11308926104452616,\n",
-    "    \"motorized_time\": -0.021067655041764506,\n",
-    "    \"nonmotorized_time\": -0.04394234551517281,\n",
-    "    \"vehbywrk_Bike\": -2.644433522672152,\n",
-    "    \"vehbywrk_SR\": -3.0157086560458417,\n",
-    "    \"vehbywrk_Transit\": -3.9631693279363467,\n",
-    "    \"vehbywrk_Walk\": -3.3398299003269862,\n",
-    "    \"wkcbd_Bike\": 0.371887416799123,\n",
-    "    \"wkcbd_SR2\": 0.37071617423118847,\n",
-    "    \"wkcbd_SR3+\": 0.22893265840284804,\n",
-    "    \"wkcbd_Transit\": 1.1056371095671524,\n",
-    "    \"wkcbd_Walk\": 0.030612758978009455,\n",
-    "    \"wkempden_Bike\": 0.001542900697956932,\n",
-    "    \"wkempden_SR2\": 0.00204432338331191,\n",
-    "    \"wkempden_SR3+\": 0.0035300285386588638,\n",
-    "    \"wkempden_Transit\": 0.00316015805069698,\n",
-    "    \"wkempden_Walk\": 0.0037858816800672803,\n",
+    "    \"ASC_Bike\": 0.9768656487673031,\n",
+    "    \"ASC_SR2\": 0.5928602674741049,\n",
+    "    \"ASC_SR3+\": -0.7852130283746404,\n",
+    "    \"ASC_Transit\": 2.2579601121574915,\n",
+    "    \"ASC_Walk\": 2.9068933591665163,\n",
+    "    \"costbyincome\": -0.022668384302186657,\n",
+    "    \"hhinc#4\": -0.006445844794354888,\n",
+    "    \"hhinc#5\": -0.011662175656345601,\n",
+    "    \"hhinc#6\": -0.01200189697215675,\n",
+    "    \"motorized_ovtbydist\": -0.11312496962190924,\n",
+    "    \"motorized_time\": -0.021071030481812515,\n",
+    "    \"nonmotorized_time\": -0.04400616719163487,\n",
+    "    \"vehbywrk_Bike\": -2.6637415199527004,\n",
+    "    \"vehbywrk_SR\": -3.014536760048916,\n",
+    "    \"vehbywrk_Transit\": -3.9632962228600492,\n",
+    "    \"vehbywrk_Walk\": -3.341935874346473,\n",
+    "    \"wkcbd_Bike\": 0.39513498215985876,\n",
+    "    \"wkcbd_SR2\": 0.3724423420159654,\n",
+    "    \"wkcbd_SR3+\": 0.22943430769880338,\n",
+    "    \"wkcbd_Transit\": 1.1064347514617832,\n",
+    "    \"wkcbd_Walk\": 0.029732296011644076,\n",
+    "    \"wkempden_Bike\": 0.0015075846525952844,\n",
+    "    \"wkempden_SR2\": 0.0020444554589942516,\n",
+    "    \"wkempden_SR3+\": 0.0035323129337276214,\n",
+    "    \"wkempden_Transit\": 0.0031600744478203252,\n",
+    "    \"wkempden_Walk\": 0.0037891491261088186,\n",
     "}\n",
     "for k in expected_x:\n",
     "    assert revealed_x[k] == approx(expected_x[k], 2e-2), (\n",
diff --git a/docs/examples/mtc/017B_alt_segmentation.ipynb b/docs/examples/mtc/017B_alt_segmentation.ipynb
index 38ef3d48..4c73344d 100644
--- a/docs/examples/mtc/017B_alt_segmentation.ipynb
+++ b/docs/examples/mtc/017B_alt_segmentation.ipynb
@@ -271,7 +271,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-10})\n",
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})\n",
     "m.calculate_parameter_covariance()\n",
     "m.loglike()"
    ]
@@ -385,32 +385,32 @@
     "from pytest import approx\n",
     "\n",
     "expected_x = {\n",
-    "    \"ASC_Bike\": -3.131353684453019,\n",
-    "    \"ASC_SR2\": -1.982918109708732,\n",
-    "    \"ASC_SR3+\": -3.7260108180838087,\n",
-    "    \"ASC_Transit\": -2.156692021346743,\n",
-    "    \"ASC_Walk\": -1.5835989189276731,\n",
-    "    \"costbyincome\": -0.0978607170004987,\n",
-    "    \"hhinc#4\": 0.0003613865368074286,\n",
-    "    \"hhinc#5\": -0.002650844116790726,\n",
-    "    \"hhinc#6\": 0.0009101590517897486,\n",
-    "    \"motorized_ovtbydist\": -0.1937603871636971,\n",
-    "    \"motorized_time\": -0.018754569843429904,\n",
-    "    \"nonmotorized_time\": -0.045210350568177106,\n",
-    "    \"vehbywrk_Bike\": -0.21719450776227095,\n",
-    "    \"vehbywrk_SR\": -0.23823848663321495,\n",
-    "    \"vehbywrk_Transit\": -0.23619743325813528,\n",
-    "    \"vehbywrk_Walk\": -0.07998140457786423,\n",
-    "    \"wkcbd_Bike\": 0.5651962301019509,\n",
-    "    \"wkcbd_SR2\": 0.16357641468531287,\n",
-    "    \"wkcbd_SR3+\": 1.3328985605250347,\n",
-    "    \"wkcbd_Transit\": 1.2613977817336346,\n",
-    "    \"wkcbd_Walk\": 0.21594406473797045,\n",
-    "    \"wkempden_Bike\": 0.0014358656965858384,\n",
-    "    \"wkempden_SR2\": 0.0010739771398805848,\n",
-    "    \"wkempden_SR3+\": 0.0013450278303126933,\n",
-    "    \"wkempden_Transit\": 0.0028932086005130325,\n",
-    "    \"wkempden_Walk\": -0.0010607308832536778,\n",
+    "    \"ASC_Bike\": -3.217907973643871,\n",
+    "    \"ASC_SR2\": -1.9786338068489517,\n",
+    "    \"ASC_SR3+\": -3.7197256505577503,\n",
+    "    \"ASC_Transit\": -2.1627440326026615,\n",
+    "    \"ASC_Walk\": -1.5346348487757604,\n",
+    "    \"costbyincome\": -0.0980868832663979,\n",
+    "    \"hhinc#4\": 0.00036483417852488175,\n",
+    "    \"hhinc#5\": -0.0019243608307967538,\n",
+    "    \"hhinc#6\": 0.0006700596973401033,\n",
+    "    \"motorized_ovtbydist\": -0.19436932701921655,\n",
+    "    \"motorized_time\": -0.01870105976828476,\n",
+    "    \"nonmotorized_time\": -0.0450318341950748,\n",
+    "    \"vehbywrk_Bike\": -0.19051136456475654,\n",
+    "    \"vehbywrk_SR\": -0.2413127730632226,\n",
+    "    \"vehbywrk_Transit\": -0.2399220019833527,\n",
+    "    \"vehbywrk_Walk\": -0.09750246029007012,\n",
+    "    \"wkcbd_Bike\": 0.48668277791486686,\n",
+    "    \"wkcbd_SR2\": 0.1627376132756929,\n",
+    "    \"wkcbd_SR3+\": 1.3302150079003858,\n",
+    "    \"wkcbd_Transit\": 1.2785207733777604,\n",
+    "    \"wkcbd_Walk\": 0.11137430958187539,\n",
+    "    \"wkempden_Bike\": 0.0016104476725365279,\n",
+    "    \"wkempden_SR2\": 0.0010724856518716283,\n",
+    "    \"wkempden_SR3+\": 0.0013414366658924435,\n",
+    "    \"wkempden_Transit\": 0.0028790997880903833,\n",
+    "    \"wkempden_Walk\": -0.0008915352403697739,\n",
     "}\n",
     "for k in expected_x:\n",
     "    assert revealed_x[k] == approx(expected_x[k], 2e-2), (\n",
diff --git a/docs/examples/mtc/017C_segmentation_males.ipynb b/docs/examples/mtc/017C_segmentation_males.ipynb
index 015dd8b7..6b87c430 100644
--- a/docs/examples/mtc/017C_segmentation_males.ipynb
+++ b/docs/examples/mtc/017C_segmentation_males.ipynb
@@ -279,7 +279,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-10})\n",
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})\n",
     "m.calculate_parameter_covariance()\n",
     "m.loglike()"
    ]
@@ -383,32 +383,32 @@
     "from pytest import approx\n",
     "\n",
     "expected_x = {\n",
-    "    \"ASC_Bike\": -1.928007825476385,\n",
-    "    \"ASC_SR2\": -1.912455790177904,\n",
-    "    \"ASC_SR3+\": -3.5514203840398006,\n",
-    "    \"ASC_Transit\": -0.8658094170164065,\n",
-    "    \"ASC_Walk\": -1.2203008866494844,\n",
-    "    \"costbyincome\": -0.06390233682221723,\n",
-    "    \"hhinc#4\": -0.0020804355665526397,\n",
-    "    \"hhinc#5\": -0.0013784543619515673,\n",
-    "    \"hhinc#6\": -0.00501951838450115,\n",
-    "    \"motorized_ovtbydist\": -0.18639734021256957,\n",
-    "    \"motorized_time\": -0.01951022691589421,\n",
-    "    \"nonmotorized_time\": -0.024446076816243063,\n",
-    "    \"vehbywrk_Bike\": -0.9930978890574966,\n",
-    "    \"vehbywrk_SR\": -0.20981196496890442,\n",
-    "    \"vehbywrk_Transit\": -0.8322463141471179,\n",
-    "    \"vehbywrk_Walk\": -0.6105805888289391,\n",
-    "    \"wkcbd_Bike\": 0.3204533731833982,\n",
-    "    \"wkcbd_SR2\": 0.027661592161988033,\n",
-    "    \"wkcbd_SR3+\": 1.423466120894847,\n",
-    "    \"wkcbd_Transit\": 1.196921606447681,\n",
-    "    \"wkcbd_Walk\": 0.22281387280820592,\n",
-    "    \"wkempden_Bike\": 0.0004958705725495997,\n",
-    "    \"wkempden_SR2\": 0.0009305263934769419,\n",
-    "    \"wkempden_SR3+\": 0.0005787415453590467,\n",
-    "    \"wkempden_Transit\": 0.002487830750207477,\n",
-    "    \"wkempden_Walk\": 0.0012923574145590863,\n",
+    "    \"ASC_Bike\": -1.9276995165663708,\n",
+    "    \"ASC_SR2\": -1.912107049090948,\n",
+    "    \"ASC_SR3+\": -3.5512122793759646,\n",
+    "    \"ASC_Transit\": -0.8651607230209142,\n",
+    "    \"ASC_Walk\": -1.2208813189769396,\n",
+    "    \"costbyincome\": -0.06394951247282908,\n",
+    "    \"hhinc#4\": -0.002078825220542985,\n",
+    "    \"hhinc#5\": -0.0013804057484782476,\n",
+    "    \"hhinc#6\": -0.00501075973719609,\n",
+    "    \"motorized_ovtbydist\": -0.18645095338370044,\n",
+    "    \"motorized_time\": -0.01950627756034513,\n",
+    "    \"nonmotorized_time\": -0.02444934723256464,\n",
+    "    \"vehbywrk_Bike\": -0.9919697070926079,\n",
+    "    \"vehbywrk_SR\": -0.21042562064513107,\n",
+    "    \"vehbywrk_Transit\": -0.8328227557451945,\n",
+    "    \"vehbywrk_Walk\": -0.6111717464254133,\n",
+    "    \"wkcbd_Bike\": 0.3109544126074143,\n",
+    "    \"wkcbd_SR2\": 0.027833469948828946,\n",
+    "    \"wkcbd_SR3+\": 1.4236860791782913,\n",
+    "    \"wkcbd_Transit\": 1.1964829661030874,\n",
+    "    \"wkcbd_Walk\": 0.22417339526190874,\n",
+    "    \"wkempden_Bike\": 0.0005115946523967164,\n",
+    "    \"wkempden_SR2\": 0.0009307007040956167,\n",
+    "    \"wkempden_SR3+\": 0.0005787490539377188,\n",
+    "    \"wkempden_Transit\": 0.0024879489537999217,\n",
+    "    \"wkempden_Walk\": 0.0012900694817472645,\n",
     "}\n",
     "for k in expected_x:\n",
     "    assert revealed_x[k] == approx(expected_x[k], 2e-2), (\n",
diff --git a/docs/examples/mtc/017D_segmentation_female.ipynb b/docs/examples/mtc/017D_segmentation_female.ipynb
index 92b9821d..a4e5b394 100644
--- a/docs/examples/mtc/017D_segmentation_female.ipynb
+++ b/docs/examples/mtc/017D_segmentation_female.ipynb
@@ -273,7 +273,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-10})\n",
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})\n",
     "m.calculate_parameter_covariance()\n",
     "m.loglike()"
    ]
@@ -377,32 +377,32 @@
     "from pytest import approx\n",
     "\n",
     "expected_x = {\n",
-    "    \"ASC_Bike\": -1.0412780932548384,\n",
-    "    \"ASC_SR2\": -1.5539810652068256,\n",
-    "    \"ASC_SR3+\": -3.1854305680124058,\n",
-    "    \"ASC_Transit\": -0.46976829166153894,\n",
-    "    \"ASC_Walk\": 1.3688704069415694,\n",
-    "    \"costbyincome\": -0.043530473293242354,\n",
-    "    \"hhinc#4\": -0.008929802961855733,\n",
-    "    \"hhinc#5\": -0.03840955269802502,\n",
-    "    \"hhinc#6\": -0.005082499964774252,\n",
-    "    \"motorized_ovtbydist\": -0.08879718827392451,\n",
-    "    \"motorized_time\": -0.01913530894108756,\n",
-    "    \"nonmotorized_time\": -0.0712577146848601,\n",
-    "    \"vehbywrk_Bike\": -0.10343275503303231,\n",
-    "    \"vehbywrk_SR\": -0.6135515751452322,\n",
-    "    \"vehbywrk_Transit\": -1.1794317963027092,\n",
-    "    \"vehbywrk_Walk\": -0.917891133747879,\n",
-    "    \"wkcbd_Bike\": 1.0534091265461347,\n",
-    "    \"wkcbd_SR2\": 0.45194163205040755,\n",
-    "    \"wkcbd_SR3+\": 0.3685888989631644,\n",
-    "    \"wkcbd_Transit\": 1.3811852123386674,\n",
-    "    \"wkcbd_Walk\": -0.008517015595822975,\n",
-    "    \"wkempden_Bike\": 0.004039748186460425,\n",
-    "    \"wkempden_SR2\": 0.0029908432533657533,\n",
-    "    \"wkempden_SR3+\": 0.005134839725445598,\n",
-    "    \"wkempden_Transit\": 0.004565945257271104,\n",
-    "    \"wkempden_Walk\": 0.005465423507422594,\n",
+    "    \"ASC_Bike\": -1.152572636408149,\n",
+    "    \"ASC_SR2\": -1.5644650318764874,\n",
+    "    \"ASC_SR3+\": -3.1993564525044924,\n",
+    "    \"ASC_Transit\": -0.47693691889916284,\n",
+    "    \"ASC_Walk\": 1.3050491979568322,\n",
+    "    \"costbyincome\": -0.04367275073345312,\n",
+    "    \"hhinc#4\": -0.008889400676650817,\n",
+    "    \"hhinc#5\": -0.03778477566573914,\n",
+    "    \"hhinc#6\": -0.0049104333300172285,\n",
+    "    \"motorized_ovtbydist\": -0.0899466124426599,\n",
+    "    \"motorized_time\": -0.01908875942766032,\n",
+    "    \"nonmotorized_time\": -0.07031849982973849,\n",
+    "    \"vehbywrk_Bike\": -0.056096943536220335,\n",
+    "    \"vehbywrk_SR\": -0.6066811735012272,\n",
+    "    \"vehbywrk_Transit\": -1.1725599447644148,\n",
+    "    \"vehbywrk_Walk\": -0.9040132688414684,\n",
+    "    \"wkcbd_Bike\": 1.0389677106158575,\n",
+    "    \"wkcbd_SR2\": 0.45438092517398476,\n",
+    "    \"wkcbd_SR3+\": 0.3768492735090911,\n",
+    "    \"wkcbd_Transit\": 1.380515995502304,\n",
+    "    \"wkcbd_Walk\": -0.008059709519232018,\n",
+    "    \"wkempden_Bike\": 0.004115728705172831,\n",
+    "    \"wkempden_SR2\": 0.0029888392082965624,\n",
+    "    \"wkempden_SR3+\": 0.005128745158280318,\n",
+    "    \"wkempden_Transit\": 0.004560345000692658,\n",
+    "    \"wkempden_Walk\": 0.005459522347934755,\n",
     "}\n",
     "for k in expected_x:\n",
     "    assert revealed_x[k] == approx(expected_x[k], 2e-2), (\n",
diff --git a/docs/examples/mtc/017_mnl.ipynb b/docs/examples/mtc/017_mnl.ipynb
index 5ffa27c1..394d472b 100644
--- a/docs/examples/mtc/017_mnl.ipynb
+++ b/docs/examples/mtc/017_mnl.ipynb
@@ -228,7 +228,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-10})"
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})"
    ]
   },
   {
@@ -274,32 +274,32 @@
     "# TEST\n",
     "revealed_x = dict(zip(m.pnames, r.x))\n",
     "expected_x = {\n",
-    "    \"ASC_Bike\": -1.6262943624447215,\n",
-    "    \"ASC_SR2\": -1.8079290469266305,\n",
-    "    \"ASC_SR3+\": -3.4332103811742303,\n",
-    "    \"ASC_Transit\": -0.6885545098242597,\n",
-    "    \"ASC_Walk\": 0.0726870281895153,\n",
-    "    \"costbyincome\": -0.05237387934320275,\n",
-    "    \"hhinc#4\": -0.005305496592916417,\n",
-    "    \"hhinc#5\": -0.008655283203651252,\n",
-    "    \"hhinc#6\": -0.006001173483271529,\n",
-    "    \"motorized_ovtbydist\": -0.13278580524985106,\n",
-    "    \"motorized_time\": -0.02016680241721925,\n",
-    "    \"nonmotorized_time\": -0.045501011178961354,\n",
-    "    \"vehbywrk_Bike\": -0.7029295169096494,\n",
-    "    \"vehbywrk_SR\": -0.3164671378998241,\n",
-    "    \"vehbywrk_Transit\": -0.9453446457215215,\n",
-    "    \"vehbywrk_Walk\": -0.7216758952894061,\n",
-    "    \"wkcbd_Bike\": 0.49873916547630665,\n",
-    "    \"wkcbd_SR2\": 0.2592843053485899,\n",
-    "    \"wkcbd_SR3+\": 1.0671968496505624,\n",
-    "    \"wkcbd_Transit\": 1.3098935436679964,\n",
-    "    \"wkcbd_Walk\": 0.09596819068963038,\n",
-    "    \"wkempden_Bike\": 0.0019099447823146232,\n",
-    "    \"wkempden_SR2\": 0.0015784089844443042,\n",
-    "    \"wkempden_SR3+\": 0.002259029274882882,\n",
-    "    \"wkempden_Transit\": 0.0031323024172347728,\n",
-    "    \"wkempden_Walk\": 0.002898970912944004,\n",
+    "    \"ASC_Bike\": -1.6288748146286787,\n",
+    "    \"ASC_SR2\": -1.8078135270324351,\n",
+    "    \"ASC_SR3+\": -3.433736103234936,\n",
+    "    \"ASC_Transit\": -0.6848112490249476,\n",
+    "    \"ASC_Walk\": 0.06818674347896501,\n",
+    "    \"costbyincome\": -0.05241892785207883,\n",
+    "    \"hhinc#4\": -0.0053236072124385365,\n",
+    "    \"hhinc#5\": -0.008643037163447177,\n",
+    "    \"hhinc#6\": -0.005997547187527915,\n",
+    "    \"motorized_ovtbydist\": -0.13286770768294562,\n",
+    "    \"motorized_time\": -0.020187078656716238,\n",
+    "    \"nonmotorized_time\": -0.045445621458847285,\n",
+    "    \"vehbywrk_Bike\": -0.7021263966019797,\n",
+    "    \"vehbywrk_SR\": -0.31663641651516017,\n",
+    "    \"vehbywrk_Transit\": -0.9462474834449455,\n",
+    "    \"vehbywrk_Walk\": -0.7218103184320303,\n",
+    "    \"wkcbd_Bike\": 0.4892847634281896,\n",
+    "    \"wkcbd_SR2\": 0.25982711048601564,\n",
+    "    \"wkcbd_SR3+\": 1.0692635814328264,\n",
+    "    \"wkcbd_Transit\": 1.3088065158093625,\n",
+    "    \"wkcbd_Walk\": 0.10174694657048731,\n",
+    "    \"wkempden_Bike\": 0.0019280381625450963,\n",
+    "    \"wkempden_SR2\": 0.0015776443302834371,\n",
+    "    \"wkempden_SR3+\": 0.0022568427708279386,\n",
+    "    \"wkempden_Transit\": 0.0031324416314067894,\n",
+    "    \"wkempden_Walk\": 0.0028903248287849214,\n",
     "}\n",
     "for k in expected_x:\n",
     "    assert revealed_x[k] == approx(expected_x[k], 2e-2), (\n",
diff --git a/docs/examples/mtc/030_mnl_constrain.ipynb b/docs/examples/mtc/030_mnl_constrain.ipynb
index c81d1cd6..12b08216 100644
--- a/docs/examples/mtc/030_mnl_constrain.ipynb
+++ b/docs/examples/mtc/030_mnl_constrain.ipynb
@@ -123,7 +123,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result_explicit = m_explicit.maximize_loglike(stderr=True)"
+    "result_explicit = m_explicit.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})"
    ]
   },
   {
@@ -140,17 +140,17 @@
     "# TEST\n",
     "assert dict(result_explicit.x) == approx(\n",
     "    {\n",
-    "        \"ASC_BIKE\": -3.160341033282137,\n",
-    "        \"ASC_SR2\": -2.455346066177878,\n",
-    "        \"ASC_SR3P\": -4.07978282935071,\n",
-    "        \"ASC_TRAN\": -1.893119496181938,\n",
-    "        \"ASC_WALK\": -1.9872045506898044,\n",
-    "        \"hhinc#2\": -0.001987067161990932,\n",
-    "        \"hhinc#3\": 0.0004572703656089083,\n",
-    "        \"hhinc#4\": -0.0061615116904621394,\n",
-    "        \"hhinc#5\": -0.014090413152090066,\n",
-    "        \"hhinc#6\": -0.009378906115232344,\n",
-    "        \"tottime\": -0.001738655408619947,\n",
+    "        \"ASC_BIKE\": -3.1599687935759446,\n",
+    "        \"ASC_SR2\": -2.455973172625806,\n",
+    "        \"ASC_SR3P\": -4.079060683312584,\n",
+    "        \"ASC_TRAN\": -1.8939988250520723,\n",
+    "        \"ASC_WALK\": -1.9902214700622933,\n",
+    "        \"hhinc#2\": -0.001983009074692179,\n",
+    "        \"hhinc#3\": 0.0004441610833811871,\n",
+    "        \"hhinc#4\": -0.006153734209369185,\n",
+    "        \"hhinc#5\": -0.014113387546025329,\n",
+    "        \"hhinc#6\": -0.009343173054524425,\n",
+    "        \"tottime\": -0.001739070694166946,\n",
     "    },\n",
     "    rel=1e-3,\n",
     ")\n",
@@ -226,7 +226,7 @@
    },
    "outputs": [],
    "source": [
-    "result = m.maximize_loglike(stderr=True)"
+    "result = m.maximize_loglike(stderr=True, options={\"maxiter\": 1000, \"ftol\": 1e-13})"
    ]
   },
   {
@@ -257,7 +257,10 @@
     "assert result.loglike == approx(-3788.573358)\n",
     "assert result.logloss == approx(0.753345269140234)\n",
     "assert result.message == \"Optimization terminated successfully\"\n",
-    "assert m.total_weight() == 5029.0"
+    "assert m.total_weight() == 5029.0\n",
+    "\n",
+    "assert result.loglike == approx(result_explicit.loglike, rel=0, abs=1e-6)\n",
+    "assert result.x[\"totcost\"] / result.x[\"tottime\"] == approx(3.0, rel=1e-6)"
    ]
   },
   {
diff --git a/docs/examples/swissmetro/151-swissmetro-panel-latent-class.ipynb b/docs/examples/swissmetro/151-swissmetro-panel-latent-class.ipynb
index 817c3176..a797a765 100644
--- a/docs/examples/swissmetro/151-swissmetro-panel-latent-class.ipynb
+++ b/docs/examples/swissmetro/151-swissmetro-panel-latent-class.ipynb
@@ -29,6 +29,24 @@
     "import larch as lx"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "double-precision",
+   "metadata": {
+    "doc_only": true,
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Use double precision for likelihood and derivative checks in this notebook.\n",
+    "import jax\n",
+    "\n",
+    "jax.config.update(\"jax_enable_x64\", True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -293,6 +311,7 @@
     "    {101: m1, 102: m2, 103: m3},\n",
     "    datatree=data.dc.set_altids([1, 2, 3]),\n",
     "    groupid=\"ID\",\n",
+    "    float_dtype=np.float64,\n",
     ")"
    ]
   },
@@ -354,15 +373,16 @@
     "# TEST\n",
     "assert b.d_loglike() == approx(\n",
     "    [\n",
-    "        -1.104770e02,\n",
-    "        -1.545916e03,\n",
-    "        -2.188546e01,\n",
-    "        -9.183448e02,\n",
-    "        -1.658521e02,\n",
-    "        8.292606e01,\n",
-    "        4.470348e-08,\n",
+    "        -110.4769662932,\n",
+    "        -1545.916343341,\n",
+    "        -21.89149030073,\n",
+    "        -918.3446977434,\n",
+    "        -165.8520908729,\n",
+    "        82.92604543645,\n",
+    "        0.0,\n",
     "    ],\n",
     "    rel=1e-5,\n",
+    "    abs=1e-8,\n",
     ")"
    ]
   },
diff --git a/docs/examples/xlogit-artificial/xlogit-test.ipynb b/docs/examples/xlogit-artificial/xlogit-test.ipynb
index d64e4f25..c5d4fdde 100644
--- a/docs/examples/xlogit-artificial/xlogit-test.ipynb
+++ b/docs/examples/xlogit-artificial/xlogit-test.ipynb
@@ -144,215 +144,225 @@
    "metadata": {
     "tags": [
      "remove-cell"
-    ]
+    ],
+    "doc_only": true
    },
    "outputs": [],
    "source": [
     "# TEST\n",
-    "se, hess, ihess = m.jax_param_cov(m.pvals)\n",
-    "assert hess == approx(\n",
-    "    np.array(\n",
-    "        [\n",
-    "            [\n",
-    "                5.214861e03,\n",
-    "                -8.737501e03,\n",
-    "                -1.207186e04,\n",
-    "                4.689243e03,\n",
-    "                -4.701574e03,\n",
-    "                2.698417e03,\n",
-    "                -2.037047e03,\n",
-    "                -6.022355e03,\n",
-    "                -8.715623e03,\n",
-    "                4.149471e00,\n",
-    "                -1.291960e01,\n",
-    "                -1.636162e-01,\n",
-    "                2.693130e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                -8.737502e03,\n",
-    "                1.918251e04,\n",
-    "                2.199170e04,\n",
-    "                -1.065871e04,\n",
-    "                6.688360e03,\n",
-    "                -2.722472e03,\n",
-    "                4.219213e01,\n",
-    "                1.198888e04,\n",
-    "                1.867886e04,\n",
-    "                5.044937e00,\n",
-    "                2.636754e01,\n",
-    "                8.628845e-01,\n",
-    "                -8.679251e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                -1.207186e04,\n",
-    "                2.199169e04,\n",
-    "                3.128377e04,\n",
-    "                -1.174997e04,\n",
-    "                1.223288e04,\n",
-    "                -7.146222e03,\n",
-    "                5.618984e03,\n",
-    "                1.528325e04,\n",
-    "                2.193285e04,\n",
-    "                -1.277624e01,\n",
-    "                3.275142e01,\n",
-    "                6.413689e-01,\n",
-    "                -6.471689e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                4.689244e03,\n",
-    "                -1.065871e04,\n",
-    "                -1.174997e04,\n",
-    "                6.683431e03,\n",
-    "                -3.098420e03,\n",
-    "                9.166903e02,\n",
-    "                8.549390e02,\n",
-    "                -6.647478e03,\n",
-    "                -1.064853e04,\n",
-    "                -4.171021e00,\n",
-    "                -1.501747e01,\n",
-    "                -1.888428e-01,\n",
-    "                5.545634e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                -4.701574e03,\n",
-    "                6.688361e03,\n",
-    "                1.223288e04,\n",
-    "                -3.098420e03,\n",
-    "                6.745899e03,\n",
-    "                -4.474388e03,\n",
-    "                4.947241e03,\n",
-    "                5.334855e03,\n",
-    "                6.653889e03,\n",
-    "                -1.206940e01,\n",
-    "                1.123395e01,\n",
-    "                3.519630e-01,\n",
-    "                2.137472e02,\n",
-    "            ],\n",
-    "            [\n",
-    "                2.698417e03,\n",
-    "                -2.722472e03,\n",
-    "                -7.146222e03,\n",
-    "                9.166902e02,\n",
-    "                -4.474388e03,\n",
-    "                4.030793e03,\n",
-    "                -4.448886e03,\n",
-    "                -2.682003e03,\n",
-    "                -2.698321e03,\n",
-    "                1.166885e01,\n",
-    "                -5.420895e00,\n",
-    "                -1.787338e-01,\n",
-    "                -1.729178e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                -2.037046e03,\n",
-    "                4.219213e01,\n",
-    "                5.618985e03,\n",
-    "                8.549389e02,\n",
-    "                4.947241e03,\n",
-    "                -4.448886e03,\n",
-    "                6.745437e03,\n",
-    "                1.371551e03,\n",
-    "                3.171216e01,\n",
-    "                -1.749387e01,\n",
-    "                2.293404e00,\n",
-    "                8.043671e-02,\n",
-    "                4.169263e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                -6.022355e03,\n",
-    "                1.198888e04,\n",
-    "                1.528325e04,\n",
-    "                -6.647478e03,\n",
-    "                5.334855e03,\n",
-    "                -2.682003e03,\n",
-    "                1.371551e03,\n",
-    "                8.437011e03,\n",
-    "                1.195731e04,\n",
-    "                1.625114e00,\n",
-    "                1.781580e01,\n",
-    "                7.714996e-01,\n",
-    "                -4.662336e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                -8.715623e03,\n",
-    "                1.867886e04,\n",
-    "                2.193285e04,\n",
-    "                -1.064853e04,\n",
-    "                6.653889e03,\n",
-    "                -2.698321e03,\n",
-    "                3.171217e01,\n",
-    "                1.195731e04,\n",
-    "                1.910547e04,\n",
-    "                4.064438e00,\n",
-    "                2.614574e01,\n",
-    "                5.830841e-01,\n",
-    "                -8.672464e03,\n",
-    "            ],\n",
-    "            [\n",
-    "                4.149475e00,\n",
-    "                5.044861e00,\n",
-    "                -1.277600e01,\n",
-    "                -4.171112e00,\n",
-    "                -1.206927e01,\n",
-    "                1.166878e01,\n",
-    "                -1.749390e01,\n",
-    "                1.624878e00,\n",
-    "                4.064697e00,\n",
-    "                -2.158142e03,\n",
-    "                1.105316e02,\n",
-    "                -2.832360e01,\n",
-    "                -1.466002e01,\n",
-    "            ],\n",
-    "            [\n",
-    "                -1.291957e01,\n",
-    "                2.636751e01,\n",
-    "                3.275133e01,\n",
-    "                -1.501750e01,\n",
-    "                1.123393e01,\n",
-    "                -5.420914e00,\n",
-    "                2.293438e00,\n",
-    "                1.781590e01,\n",
-    "                2.614552e01,\n",
-    "                1.105317e02,\n",
-    "                3.290158e03,\n",
-    "                1.039862e02,\n",
-    "                -1.082738e01,\n",
-    "            ],\n",
-    "            [\n",
-    "                -1.636047e-01,\n",
-    "                8.629456e-01,\n",
-    "                6.414795e-01,\n",
-    "                -1.887054e-01,\n",
-    "                3.519135e-01,\n",
-    "                -1.787491e-01,\n",
-    "                8.040237e-02,\n",
-    "                7.715759e-01,\n",
-    "                5.830688e-01,\n",
-    "                -2.832359e01,\n",
-    "                1.039862e02,\n",
-    "                1.726320e03,\n",
-    "                -4.051666e-01,\n",
-    "            ],\n",
+    "# Use a separate double-precision model for cancellation-sensitive curvature.\n",
+    "import jax\n",
+    "\n",
+    "with jax.experimental.enable_x64():\n",
+    "    curvature_model = m.copy()\n",
+    "    curvature_model.make_random_draws()\n",
+    "    curvature_model._draws = np.asarray(curvature_model._draws, dtype=np.float64)\n",
+    "    curvature_model.pvals = curvature_model.pvals.astype(np.float64)\n",
+    "    se, hess, ihess = curvature_model.jax_param_cov(curvature_model.pvals)\n",
+    "    # Checked independently using conditional feature and draw-score covariances.\n",
+    "    assert hess == approx(\n",
+    "        np.array(\n",
     "            [\n",
-    "                2.693130e03,\n",
-    "                -8.679251e03,\n",
-    "                -6.471688e03,\n",
-    "                5.545634e03,\n",
-    "                2.137472e02,\n",
-    "                -1.729178e03,\n",
-    "                4.169263e03,\n",
-    "                -4.662337e03,\n",
-    "                -8.672464e03,\n",
-    "                -1.466016e01,\n",
-    "                -1.082734e01,\n",
-    "                -4.051819e-01,\n",
-    "                7.339009e03,\n",
+    "                [\n",
+    "                    5.2148605454e+03,\n",
+    "                    -8.7375012340e+03,\n",
+    "                    -1.2071862397e+04,\n",
+    "                    4.6892437252e+03,\n",
+    "                    -4.7015736950e+03,\n",
+    "                    2.6984166826e+03,\n",
+    "                    -2.0370464502e+03,\n",
+    "                    -6.0223557791e+03,\n",
+    "                    -8.7156228815e+03,\n",
+    "                    4.1494933643e+00,\n",
+    "                    -1.2919575383e+01,\n",
+    "                    -1.6361107887e-01,\n",
+    "                    2.6931296859e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -8.7375012340e+03,\n",
+    "                    1.9182513675e+04,\n",
+    "                    2.1991695648e+04,\n",
+    "                    -1.0658709634e+04,\n",
+    "                    6.6883609225e+03,\n",
+    "                    -2.7224720098e+03,\n",
+    "                    4.2192129389e+01,\n",
+    "                    1.1988881136e+04,\n",
+    "                    1.8678858428e+04,\n",
+    "                    5.0449462967e+00,\n",
+    "                    2.6367419069e+01,\n",
+    "                    8.6286065894e-01,\n",
+    "                    -8.6792513827e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -1.2071862397e+04,\n",
+    "                    2.1991695648e+04,\n",
+    "                    3.1283773817e+04,\n",
+    "                    -1.1749966293e+04,\n",
+    "                    1.2232880394e+04,\n",
+    "                    -7.1462211573e+03,\n",
+    "                    5.6189845694e+03,\n",
+    "                    1.5283254474e+04,\n",
+    "                    2.1932846718e+04,\n",
+    "                    -1.2776261678e+01,\n",
+    "                    3.2751317660e+01,\n",
+    "                    6.4136228241e-01,\n",
+    "                    -6.4716883178e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    4.6892437252e+03,\n",
+    "                    -1.0658709634e+04,\n",
+    "                    -1.1749966293e+04,\n",
+    "                    6.6834316156e+03,\n",
+    "                    -3.0984196662e+03,\n",
+    "                    9.1669030935e+02,\n",
+    "                    8.5493892247e+02,\n",
+    "                    -6.6474775770e+03,\n",
+    "                    -1.0648528315e+04,\n",
+    "                    -4.1710770358e+00,\n",
+    "                    -1.5017398592e+01,\n",
+    "                    -1.8882399764e-01,\n",
+    "                    5.5456340841e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -4.7015736950e+03,\n",
+    "                    6.6883609225e+03,\n",
+    "                    1.2232880394e+04,\n",
+    "                    -3.0984196662e+03,\n",
+    "                    6.7458991530e+03,\n",
+    "                    -4.4743878230e+03,\n",
+    "                    4.9472408559e+03,\n",
+    "                    5.3348553460e+03,\n",
+    "                    6.6538886896e+03,\n",
+    "                    -1.2069365540e+01,\n",
+    "                    1.1233925206e+01,\n",
+    "                    3.5194535716e-01,\n",
+    "                    2.1374719656e+02,\n",
+    "                ],\n",
+    "                [\n",
+    "                    2.6984166826e+03,\n",
+    "                    -2.7224720098e+03,\n",
+    "                    -7.1462211573e+03,\n",
+    "                    9.1669030935e+02,\n",
+    "                    -4.4743878230e+03,\n",
+    "                    4.0307934605e+03,\n",
+    "                    -4.4488857656e+03,\n",
+    "                    -2.6820027301e+03,\n",
+    "                    -2.6983210949e+03,\n",
+    "                    1.1668837182e+01,\n",
+    "                    -5.4208933579e+00,\n",
+    "                    -1.7873879323e-01,\n",
+    "                    -1.7291781468e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -2.0370464502e+03,\n",
+    "                    4.2192129389e+01,\n",
+    "                    5.6189845694e+03,\n",
+    "                    8.5493892247e+02,\n",
+    "                    4.9472408559e+03,\n",
+    "                    -4.4488857656e+03,\n",
+    "                    6.7454378744e+03,\n",
+    "                    1.3715508508e+03,\n",
+    "                    3.1712169241e+01,\n",
+    "                    -1.7493858065e+01,\n",
+    "                    2.2934360423e+00,\n",
+    "                    8.0407274928e-02,\n",
+    "                    4.1692626971e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -6.0223557791e+03,\n",
+    "                    1.1988881136e+04,\n",
+    "                    1.5283254474e+04,\n",
+    "                    -6.6474775770e+03,\n",
+    "                    5.3348553460e+03,\n",
+    "                    -2.6820027301e+03,\n",
+    "                    1.3715508508e+03,\n",
+    "                    8.4370104335e+03,\n",
+    "                    1.1957313108e+04,\n",
+    "                    1.6251054124e+00,\n",
+    "                    1.7815759117e+01,\n",
+    "                    7.7149546263e-01,\n",
+    "                    -4.6623368939e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -8.7156228815e+03,\n",
+    "                    1.8678858428e+04,\n",
+    "                    2.1932846718e+04,\n",
+    "                    -1.0648528315e+04,\n",
+    "                    6.6538886896e+03,\n",
+    "                    -2.6983210949e+03,\n",
+    "                    3.1712169241e+01,\n",
+    "                    1.1957313108e+04,\n",
+    "                    1.9105466630e+04,\n",
+    "                    4.0643711399e+00,\n",
+    "                    2.6145606157e+01,\n",
+    "                    5.8310570734e-01,\n",
+    "                    -8.6724638742e+03,\n",
+    "                ],\n",
+    "                [\n",
+    "                    4.1494933643e+00,\n",
+    "                    5.0449462967e+00,\n",
+    "                    -1.2776261678e+01,\n",
+    "                    -4.1710770358e+00,\n",
+    "                    -1.2069365540e+01,\n",
+    "                    1.1668837182e+01,\n",
+    "                    -1.7493858065e+01,\n",
+    "                    1.6251054124e+00,\n",
+    "                    4.0643711399e+00,\n",
+    "                    -2.1581422642e+03,\n",
+    "                    1.1053166529e+02,\n",
+    "                    -2.8323575873e+01,\n",
+    "                    -1.4660088744e+01,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -1.2919575383e+01,\n",
+    "                    2.6367419069e+01,\n",
+    "                    3.2751317660e+01,\n",
+    "                    -1.5017398592e+01,\n",
+    "                    1.1233925206e+01,\n",
+    "                    -5.4208933579e+00,\n",
+    "                    2.2934360423e+00,\n",
+    "                    1.7815759117e+01,\n",
+    "                    2.6145606157e+01,\n",
+    "                    1.1053166529e+02,\n",
+    "                    3.2901582295e+03,\n",
+    "                    1.0398625278e+02,\n",
+    "                    -1.0827359797e+01,\n",
+    "                ],\n",
+    "                [\n",
+    "                    -1.6361107887e-01,\n",
+    "                    8.6286065894e-01,\n",
+    "                    6.4136228241e-01,\n",
+    "                    -1.8882399764e-01,\n",
+    "                    3.5194535716e-01,\n",
+    "                    -1.7873879323e-01,\n",
+    "                    8.0407274928e-02,\n",
+    "                    7.7149546263e-01,\n",
+    "                    5.8310570734e-01,\n",
+    "                    -2.8323575873e+01,\n",
+    "                    1.0398625278e+02,\n",
+    "                    1.7263197935e+03,\n",
+    "                    -4.0518471452e-01,\n",
+    "                ],\n",
+    "                [\n",
+    "                    2.6931296859e+03,\n",
+    "                    -8.6792513827e+03,\n",
+    "                    -6.4716883178e+03,\n",
+    "                    5.5456340841e+03,\n",
+    "                    2.1374719656e+02,\n",
+    "                    -1.7291781468e+03,\n",
+    "                    4.1692626971e+03,\n",
+    "                    -4.6623368939e+03,\n",
+    "                    -8.6724638742e+03,\n",
+    "                    -1.4660088744e+01,\n",
+    "                    -1.0827359797e+01,\n",
+    "                    -4.0518471452e-01,\n",
+    "                    7.3390092265e+03,\n",
+    "                ],\n",
     "            ],\n",
-    "        ],\n",
-    "        dtype=np.float32,\n",
-    "    ),\n",
-    "    rel=1e-3,\n",
-    ")"
+    "            dtype=np.float64,\n",
+    "        ),\n",
+    "        rel=1e-6,\n",
+    "    )"
    ]
   },
   {
diff --git a/envs/install-graphviz.sh b/envs/install-graphviz.sh
new file mode 100755
index 00000000..418395d0
--- /dev/null
+++ b/envs/install-graphviz.sh
@@ -0,0 +1,71 @@
+#!/usr/bin/env bash
+
+# Install the native Graphviz tools inside a virtual environment on Debian/Ubuntu.
+# APT resolves the missing packages; extraction requires no root privileges.
+set -euo pipefail
+
+if command -v dot >/dev/null 2>&1; then
+    dot -V
+    exit 0
+fi
+
+for required_command in apt-get dpkg-deb awk; do
+    if ! command -v "$required_command" >/dev/null 2>&1; then
+        echo "This installer requires Debian/Ubuntu tools; install Graphviz with your system package manager." >&2
+        exit 1
+    fi
+done
+
+graphviz_venv=$(realpath "${1:-.venv}")
+if [[ ! -x "$graphviz_venv/bin/python" ]]; then
+    echo "Create the virtual environment before installing Graphviz: $graphviz_venv" >&2
+    exit 1
+fi
+if [[ -x "$graphviz_venv/bin/dot" ]] && "$graphviz_venv/bin/dot" -V; then
+    exit 0
+fi
+
+graphviz_downloads=$(mktemp -d "${TMPDIR:-/tmp}/larch-graphviz.XXXXXX")
+cleanup() { rm -rf -- "$graphviz_downloads"; }
+trap cleanup EXIT
+
+apt-get --simulate install --no-install-recommends graphviz > "$graphviz_downloads/plan.txt"
+mapfile -t graphviz_packages < <(
+    awk '/^Inst / { version = $3; sub(/^\(/, "", version); print $2 "=" version }' "$graphviz_downloads/plan.txt"
+)
+if [[ ${#graphviz_packages[@]} == 0 ]]; then
+    echo "Graphviz is already installed; ensure its dot executable is on PATH." >&2
+    exit 1
+fi
+
+graphviz_root="$graphviz_venv/share/graphviz"
+mkdir -p "$graphviz_root"
+(
+    cd "$graphviz_downloads"
+    apt-get download "${graphviz_packages[@]}"
+    for graphviz_package in ./*.deb; do
+        dpkg-deb -x "$graphviz_package" "$graphviz_root"
+    done
+)
+
+cat > "$graphviz_venv/bin/dot" <<'WRAPPER'
+#!/usr/bin/env bash
+set -euo pipefail
+graphviz_venv=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/.." && pwd)
+graphviz_root="$graphviz_venv/share/graphviz"
+graphviz_libraries=()
+for graphviz_directory in "$graphviz_root"/usr/lib/*-linux-gnu "$graphviz_root/usr/lib"; do
+    if [[ -d "$graphviz_directory" ]]; then
+        graphviz_libraries+=("$graphviz_directory")
+        if [[ -d "$graphviz_directory/graphviz" ]]; then
+            export GVBINDIR="$graphviz_directory/graphviz"
+        fi
+    fi
+done
+printf -v graphviz_library_path '%s:' "${graphviz_libraries[@]}"
+export LD_LIBRARY_PATH="${graphviz_library_path%:}${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
+exec "$graphviz_root/usr/bin/dot" "$@"
+WRAPPER
+chmod +x "$graphviz_venv/bin/dot"
+"$graphviz_venv/bin/dot" -c
+"$graphviz_venv/bin/dot" -V
diff --git a/envs/uv-dev.sh b/envs/uv-dev.sh
index 1a71da24..39cf772d 100644
--- a/envs/uv-dev.sh
+++ b/envs/uv-dev.sh
@@ -116,6 +116,11 @@ fi
 echo "Creating/syncing the UV python venv ..."
 uv sync --all-extras
 
+# Graphviz renders notebook graphs and diagrams in Excel reports.
+if command -v apt-get >/dev/null 2>&1 && ! command -v dot >/dev/null 2>&1; then
+    bash envs/install-graphviz.sh .venv
+fi
+
 # install sharrow in editable mode in the UV virtual environment
 echo "Installing sharrow in editable mode ..."
 uv pip install -e ./subs/sharrow
diff --git a/exercises/mixed-logit.ipynb b/exercises/mixed-logit.ipynb
index 1aa72eb9..c7619441 100644
--- a/exercises/mixed-logit.ipynb
+++ b/exercises/mixed-logit.ipynb
@@ -27,7 +27,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q \"larch>=6\""
+    "import importlib.util\n",
+    "\n",
+    "if importlib.util.find_spec(\"larch\") is None:\n",
+    "    %pip install -q \"larch>=6\""
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index 5642d124..c152b676 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,7 +48,7 @@ test = [
     "zarr",
     "tabulate",
     "altair",
-    "vl-convert-python",
+    "vl-convert-python >=1.9.0",
     "pydot",
     "pytest-regressions",
     "openmatrix",
@@ -134,6 +134,7 @@ ignore-overlong-task-comments = true
 [tool.ruff.lint.pydocstyle]
 convention = "numpy"
 
+# TODO: remove when done with local testing and sharrow wheel for py314 is available
 [tool.uv.sources]
 # Local development uses the same Python 3.14 fixes as the CI dependency wheel.
 sharrow = { path = "../sharrow", editable = true, marker = "python_version >= '3.14'" }
diff --git a/uv.lock b/uv.lock
index 6cbc5abe..b8fe24f3 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1147,7 +1147,7 @@ requires-dist = [
     { name = "sparse" },
     { name = "tables", marker = "extra == 'test'" },
     { name = "tabulate", marker = "extra == 'test'" },
-    { name = "vl-convert-python", marker = "extra == 'test'" },
+    { name = "vl-convert-python", marker = "extra == 'test'", specifier = ">=1.9.0" },
     { name = "xarray" },
     { name = "xlsxwriter", marker = "extra == 'test'" },
     { name = "xmle", specifier = ">=0.1.25" },
@@ -3117,15 +3117,15 @@ wheels = [
 
 [[package]]
 name = "vl-convert-python"
-version = "1.8.0"
+version = "1.9.0.post1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/15/08/06945bff9655c5b0520a8d1b2550cd8007e106ebec45a33840035420e0d2/vl_convert_python-1.8.0.tar.gz", hash = "sha256:ceca613ca5551c55270a15ca48d0f3a7de1e949e0f127310e9b0f6570ea3fbbb", size = 4651586, upload-time = "2025-05-28T00:06:47.6Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/93/89/36722344d1758ec2106f4e8eca980f173cfe8f8d0358c1b77cc5d2e035a4/vl_convert_python-1.9.0.post1.tar.gz", hash = "sha256:a5b06b3128037519001166f5341ec7831e19fbd7f3a5f78f73d557ac2d5859ef", size = 4663469, upload-time = "2026-01-21T00:09:55.61Z" }
 wheels = [
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-    { url = "https://files.pythonhosted.org/packages/09/fa/1dd944c9e9898e59e31c385bdce215aca543acc555de20b8bf4dc60ddb89/vl_convert_python-1.8.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3388e3913287867b3553c10f81ca2d85268216a5a75e7c71b9c1b59887c1977e", size = 31668750, upload-time = "2025-05-28T00:06:36.158Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/6b/48f6d47a92eaf6f0dd235146307a7eb0d179b78d2faebc53aca3f1e49177/vl_convert_python-1.8.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b51264998e8fcc43dbce801484a950cfe6513cdc4c46b20604ef50989855a617", size = 32970141, upload-time = "2025-05-28T00:06:41.323Z" },
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+    { url = "https://files.pythonhosted.org/packages/9f/59/e5862245972ff467d38b0eb5ad28154685e23ecabb47e14f2b6962da7b56/vl_convert_python-1.9.0.post1-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:43e9515f65bbcd317d1ef328787fd7bf0344c2fde9292eb7a0e64d5d3d29fccb", size = 30512930, upload-time = "2026-01-21T00:09:43.198Z" },
+    { url = "https://files.pythonhosted.org/packages/62/e6/e7d0b538c2f0daaf120901dc113bd5d5d1fa51a9532fa5ffd90234e8c69e/vl_convert_python-1.9.0.post1-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:b0e7a3245f32addec7e7abeb1badf72b1513ed71ba1dba7aca853901217b3f4e", size = 29738742, upload-time = "2026-01-21T00:09:46.016Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/e2/5645a1bc174c53ff8cd305ed76a4a76ba36e155302db20b42b7e78daeef8/vl_convert_python-1.9.0.post1-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6ecfe4b7e2ea9e8c30fd6d6eaea3ef85475be1ad249407d9796dce4ecdb5b32", size = 33366278, upload-time = "2026-01-21T00:09:48.42Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/18/88e02899b72fa8273ffb32bde12b0e5776ee0fd9fb29559a49c48ec4c5fa/vl_convert_python-1.9.0.post1-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c1558fa0055e88c465bd3d71760cde9fa2c94a95f776a0ef9178252fd820b1f", size = 33520215, upload-time = "2026-01-21T00:09:50.992Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/db/6e8616587035bf0745d0f10b1791c7e945180ac5d6b28677d2f2b3ca693c/vl_convert_python-1.9.0.post1-cp37-abi3-win_amd64.whl", hash = "sha256:7e263269ac0d304640ca842b44dfe430ed863accd9edecff42e279bfc48ce940", size = 32051516, upload-time = "2026-01-21T00:09:53.47Z" },
 ]
 
 [[package]]

From a2d9af062d5be564213cf638ad384c2b7d1a49a6 Mon Sep 17 00:00:00 2001
From: Jan Zill <jan@outerloop.io>
Date: Thu, 15 Oct 2026 13:28:58 +1000
Subject: [PATCH 4/4] use float64 for numerical checks

---
 .../swissmetro/101_swissmetro_mnl.ipynb       | 16 +++-
 tests/test_swissmetro.py                      | 89 ++++++++++---------
 2 files changed, 62 insertions(+), 43 deletions(-)

diff --git a/docs/examples/swissmetro/101_swissmetro_mnl.ipynb b/docs/examples/swissmetro/101_swissmetro_mnl.ipynb
index af92795b..4674a070 100644
--- a/docs/examples/swissmetro/101_swissmetro_mnl.ipynb
+++ b/docs/examples/swissmetro/101_swissmetro_mnl.ipynb
@@ -20,6 +20,7 @@
    "outputs": [],
    "source": [
     "# TEST\n",
+    "import jax\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
@@ -27,6 +28,8 @@
     "pd.set_option(\"expand_frame_repr\", False)\n",
     "pd.set_option(\"display.precision\", 3)\n",
     "np.set_printoptions(precision=12)\n",
+    "# Use double precision for the numerical reference checks.\n",
+    "jax.config.update(\"jax_enable_x64\", True)\n",
     "from pytest import approx\n",
     "\n",
     "import larch as lx"
@@ -298,7 +301,7 @@
    "outputs": [],
    "source": [
     "m.set_cap(15)\n",
-    "result = m.maximize_loglike(method=\"SLSQP\")"
+    "result = m.maximize_loglike(method=\"SLSQP\", options={\"ftol\": 1e-9})"
    ]
   },
   {
@@ -313,6 +316,8 @@
    "outputs": [],
    "source": [
     "# TEST\n",
+    "assert result.success\n",
+    "assert abs(m.d_loglike()).max() / m.total_weight() < 1e-5\n",
     "assert result.loglike == approx(-5331.252006971916)"
    ]
   },
@@ -348,8 +353,13 @@
    "outputs": [],
    "source": [
     "# TEST\n",
-    "assert m.pvals == approx([-0.15475, -0.70178, -0.010842, -0.012776], rel=1e-3)\n",
-    "assert m.pstderr == approx([0.0432357, 0.0548783, 0.000518335, 0.000568829], rel=1e-3)\n",
+    "assert m.pvals == approx(\n",
+    "    [-0.1546324225, -0.7011867125, -0.01083790651, -0.01277860255], rel=1e-3\n",
+    ")\n",
+    "assert m.pstderr == approx(\n",
+    "    [0.04323547174, 0.05487393317, 0.0005183019169, 0.0005688334527],\n",
+    "    rel=1e-3,\n",
+    ")\n",
     "assert m.parameter_summary().data[\"t Stat\"].values.astype(float) == approx(\n",
     "    [-3.58, -12.79, -20.92, -22.46], rel=1e-2\n",
     ")"
diff --git a/tests/test_swissmetro.py b/tests/test_swissmetro.py
index 78c2fdf3..9adafb7c 100644
--- a/tests/test_swissmetro.py
+++ b/tests/test_swissmetro.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import jax
 import pandas as pd
 from pytest import approx
 
@@ -8,43 +9,51 @@
 
 
 def test_swissmetro_101():
-    m = lx.Model()
-    m.title = "swissmetro example 01 (simple logit)"
-    m.availability_co_vars = {
-        1: "TRAIN_AV * (SP!=0)",
-        2: "SM_AV",
-        3: "CAR_AV * (SP!=0)",
-    }
-    m.choice_co_code = "CHOICE"
-    m.utility_co[1] = P("ASC_TRAIN")
-    m.utility_co[2] = 0
-    m.utility_co[3] = P("ASC_CAR")
-    m.utility_co[1] += X("TRAIN_TT") * P("B_TIME")
-    m.utility_co[2] += X("SM_TT") * P("B_TIME")
-    m.utility_co[3] += X("CAR_TT") * P("B_TIME")
-    m.utility_co[1] += X("TRAIN_CO*(GA==0)") * P("B_COST")
-    m.utility_co[2] += X("SM_CO*(GA==0)") * P("B_COST")
-    m.utility_co[3] += X("CAR_CO") * P("B_COST")
-    m.ordering = [("ASCs", "ASC.*"), ("LOS", "B_.*")]
-    raw_data = pd.read_csv(lx.example_file("swissmetro.csv.gz")).rename_axis(
-        index="CASEID"
-    )
-    raw_data.head()
-    keep = raw_data.eval("PURPOSE in (1,3) and CHOICE != 0")
-    selected_data = raw_data[keep]
-    ds = lx.Dataset.construct.from_idco(
-        selected_data, alts={1: "Train", 2: "SM", 3: "Car"}
-    )
-    m.datatree = ds
-    m.set_cap(15)
-    result = m.maximize_loglike(method="SLSQP")
-    assert result.loglike == approx(-5331.252006971916)
-    m.calculate_parameter_covariance()
-    m.parameter_summary()
-    assert m.pvals == approx([-0.15475, -0.70178, -0.010842, -0.012776], rel=1e-3)
-    assert m.pstderr == approx(
-        [0.0432357, 0.0548783, 0.000518335, 0.000568829], rel=1e-3
-    )
-    assert m.parameter_summary().data["t Stat"].values.astype(float) == approx(
-        [-3.58, -12.79, -20.92, -22.46], rel=1e-2
-    )
+    # Float32 likelihood plateaus can stop SLSQP before this coefficient
+    # benchmark's tolerances are met. Keep precision local to this test.
+    with jax.experimental.enable_x64():
+        m = lx.Model()
+        m.title = "swissmetro example 01 (simple logit)"
+        m.availability_co_vars = {
+            1: "TRAIN_AV * (SP!=0)",
+            2: "SM_AV",
+            3: "CAR_AV * (SP!=0)",
+        }
+        m.choice_co_code = "CHOICE"
+        m.utility_co[1] = P("ASC_TRAIN")
+        m.utility_co[2] = 0
+        m.utility_co[3] = P("ASC_CAR")
+        m.utility_co[1] += X("TRAIN_TT") * P("B_TIME")
+        m.utility_co[2] += X("SM_TT") * P("B_TIME")
+        m.utility_co[3] += X("CAR_TT") * P("B_TIME")
+        m.utility_co[1] += X("TRAIN_CO*(GA==0)") * P("B_COST")
+        m.utility_co[2] += X("SM_CO*(GA==0)") * P("B_COST")
+        m.utility_co[3] += X("CAR_CO") * P("B_COST")
+        m.ordering = [("ASCs", "ASC.*"), ("LOS", "B_.*")]
+        raw_data = pd.read_csv(lx.example_file("swissmetro.csv.gz")).rename_axis(
+            index="CASEID"
+        )
+        raw_data.head()
+        keep = raw_data.eval("PURPOSE in (1,3) and CHOICE != 0")
+        selected_data = raw_data[keep]
+        ds = lx.Dataset.construct.from_idco(
+            selected_data, alts={1: "Train", 2: "SM", 3: "Car"}
+        )
+        m.datatree = ds
+        m.set_cap(15)
+        result = m.maximize_loglike(method="SLSQP", options={"ftol": 1e-9})
+        assert result.success
+        assert abs(m.d_loglike()).max() / m.total_weight() < 1e-5
+        assert result.loglike == approx(-5331.252006971916)
+        m.calculate_parameter_covariance()
+        m.parameter_summary()
+        assert m.pvals == approx(
+            [-0.1546324225, -0.7011867125, -0.01083790651, -0.01277860255], rel=1e-3
+        )
+        assert m.pstderr == approx(
+            [0.04323547174, 0.05487393317, 0.0005183019169, 0.0005688334527],
+            rel=1e-3,
+        )
+        assert m.parameter_summary().data["t Stat"].values.astype(float) == approx(
+            [-3.58, -12.79, -20.92, -22.46], rel=1e-2
+        )