Add Antweiler-Freyberger (2025) iterative quadrature estimator

CLAUDE.md

@@ -259,6 +259,10 @@ When writing new public-facing code, always accept and return `period`. Convert
 
 ## Testing
 
-- pytest with markers: `wip`, `unit`, `integration`, `end_to_end`
+- pytest with markers: `wip`, `unit`, `integration`, `end_to_end`, `long_running`
 - Test files mirror source structure in `tests/`
 - Memory profiling available via pytest-memray (Unix only)
+- MATLAB AF CES / translog reproduction tests live in the parent workspace at
+  `../matlab_ces_repro/` (alongside `sim_repro/`), not in this library. They depend on
+  reference data at `/home/hmg/sciebo/Skill estimation/` and the CNLSY xls bundled
+  beside them. Run from the workspace root.

docs/getting_started/tutorial.ipynb

@@ -22,7 +22,7 @@
     "import pandas as pd\n",
     "\n",
     "from skillmodels import get_maximization_inputs\n",
-    "from skillmodels.config import TEST_DATA_DIR\n",
+    "from skillmodels.common.config import TEST_DATA_DIR\n",
     "from skillmodels.test_data.model2 import MODEL2"
    ]
   },

docs/how_to_guides/how_to_simulate_dataset.ipynb

@@ -8,8 +8,8 @@
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from skillmodels.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
-    "from skillmodels.simulate_data import simulate_dataset\n",
+    "from skillmodels.common.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
+    "from skillmodels.common.simulate_data import simulate_dataset\n",
     "from skillmodels.test_data.model2 import MODEL2"
    ]
   },

docs/how_to_guides/how_to_visualize_correlations.ipynb

@@ -15,8 +15,8 @@
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from skillmodels.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
-    "from skillmodels.correlation_heatmap import (\n",
+    "from skillmodels.common.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
+    "from skillmodels.common.correlation_heatmap import (\n",
     "    get_measurements_corr,\n",
     "    get_quasi_scores_corr,\n",
     "    get_scores_corr,\n",
@@ -167,7 +167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from skillmodels.visualize_transition_equations import (\n",
+    "from skillmodels.common.visualize_transition_equations import (\n",
     "    _get_parsed_params,\n",
     "    _set_index_params,\n",
     ")"
@@ -179,7 +179,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from skillmodels.process_model import process_model"
+    "from skillmodels.common.process_model import process_model"
    ]
   },
   {

docs/how_to_guides/how_to_visualize_pairwise_factor_distribution.ipynb

@@ -10,16 +10,16 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "from skillmodels.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
-    "from skillmodels.maximization_inputs import get_maximization_inputs\n",
-    "from skillmodels.simulate_data import simulate_dataset\n",
-    "from skillmodels.test_data.model2 import MODEL2\n",
-    "from skillmodels.visualize_factor_distributions import (\n",
+    "from skillmodels.chs.maximization_inputs import get_maximization_inputs\n",
+    "from skillmodels.common.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
+    "from skillmodels.common.simulate_data import simulate_dataset\n",
+    "from skillmodels.common.visualize_factor_distributions import (\n",
     "    bivariate_density_contours,\n",
     "    bivariate_density_surfaces,\n",
     "    combine_distribution_plots,\n",
     "    univariate_densities,\n",
-    ")"
+    ")\n",
+    "from skillmodels.test_data.model2 import MODEL2"
    ]
   },
   {

docs/how_to_guides/how_to_visualize_transition_equations.ipynb

@@ -9,12 +9,12 @@
    "source": [
     "import pandas as pd\n",
     "\n",
-    "from skillmodels.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
-    "from skillmodels.test_data.model2 import MODEL2\n",
-    "from skillmodels.visualize_transition_equations import (\n",
+    "from skillmodels.common.config import REGRESSION_VAULT, TEST_DATA_DIR\n",
+    "from skillmodels.common.visualize_transition_equations import (\n",
     "    combine_transition_plots,\n",
     "    get_transition_plots,\n",
-    ")"
+    ")\n",
+    "from skillmodels.test_data.model2 import MODEL2"
    ]
   },
   {

docs/how_to_guides/model_specs.md

@@ -114,7 +114,7 @@ Fine-tune the estimation:
 Define custom transition equations using the `@register_params` decorator:
 
 ```python
-from skillmodels.decorators import register_params
+from skillmodels.common.decorators import register_params
 
 @register_params(params=["lincoeff"])
 def my_linear(fac, params):

environment-cuda.yml

@@ -0,0 +1,67 @@
+---
+# Conda/mamba environment for the skillmodels `af-estimator` branch
+# **with CUDA 12 GPU support**.
+#
+# Same package set as `environment.yml`, except JAX is pulled in via the
+# `jax[cuda12]` PyPI extra and `cuda-nvcc` is added on the conda side.
+# Requires the host system to provide a CUDA 12 toolkit; see
+# https://jax.readthedocs.io/en/latest/installation.html for details.
+#
+# Usage:
+#   mamba env create -f environment-cuda.yml
+#   mamba activate skillmodels-af-cuda
+name: skillmodels-af-cuda
+channels:
+  - conda-forge
+  - nodefaults
+dependencies:
+  # CUDA toolchain (required for the cuda12 JAX wheel below)
+  - cuda-nvcc >=12
+  # Python + core scientific stack
+  - python ~=3.14.0
+  - scipy >=1.16.0
+  - h5py >=3.16.0,<4
+  # Skillmodels conda deps
+  - filterpy *
+  - ipykernel >=6.29.5
+  - jupyterlab *
+  - nbformat >=5.10.4
+  - networkx *
+  - pybaum >=0.1.3
+  - scikit-learn >=1.5  # AMN Stage 1 (mixture EM)
+  # Test / profiling tooling
+  - pytest >=8.4.1
+  - pytest-cov >=6.2.1
+  - pytest-xdist >=3.8.0
+  - pytest-memray *
+  - snakeviz *
+  - xlrd >=2
+  - prek *
+  # Downstream-only conda deps (skane-struct-bw / health-cognition):
+  - deepdiff >=8.5.0
+  - memray >=1.17.2
+  - statsmodels >=0.14.5
+  - tabulate >=0.9.0
+  - seaborn *
+  - pip
+  - pip:
+      # Skillmodels project deps (PyPI), with CUDA-12 JAX wheel
+      - dags>=0.5.1
+      - jax[cuda12]>=0.9
+      - jupyter-book>=2
+      - kaleido>=1.2
+      - numpy>=2.4
+      - pandas>=3
+      - plotly>=6.6
+      - pytask>=0.5.8
+      - pytask-parallel>=0.5.2
+      - pdbp
+      # Pinned to the optimagic branch the AF estimator relies on.
+      # yamllint disable-line rule:line-length
+      - optimagic @ git+https://github.com/optimagic-dev/optimagic.git@probability-allow-fixed-entries
+      # Downstream-only PyPI deps:
+      - fides>=0.7.8
+      - statadict>=1.1.0
+      # Skillmodels itself, from the af-estimator branch.
+      # yamllint disable-line rule:line-length
+      - skillmodels @ git+https://github.com/OpenSourceEconomics/skillmodels.git@af-estimator

environment.yml

@@ -0,0 +1,70 @@
+---
+# Conda/mamba environment for the skillmodels `af-estimator` branch.
+#
+# Installs every package needed to run the skillmodels test suite **and**
+# the two downstream research applications (`skane-struct-bw`,
+# `health-cognition`) -- minus the two applications themselves, which
+# are supplied separately by their respective project teams.
+#
+# CPU-only JAX. For an environment with CUDA-12 support use
+# `environment-cuda.yml` instead.
+#
+# Usage:
+#   mamba env create -f environment.yml
+#   mamba activate skillmodels-af
+name: skillmodels-af
+channels:
+  - conda-forge
+  - nodefaults
+dependencies:
+  # Python + core scientific stack
+  - python ~=3.14.0
+  - scipy >=1.16.0
+  - h5py >=3.16.0,<4
+  # Skillmodels conda deps
+  - filterpy *
+  - ipykernel >=6.29.5
+  - jupyterlab *
+  - nbformat >=5.10.4
+  - networkx *
+  - pybaum >=0.1.3
+  - scikit-learn >=1.5  # AMN Stage 1 (mixture EM)
+  # Test / profiling tooling (skillmodels' tests-cpu feature)
+  - pytest >=8.4.1
+  - pytest-cov >=6.2.1
+  - pytest-xdist >=3.8.0
+  - pytest-memray *
+  - snakeviz *
+  - xlrd >=2
+  - prek *
+  # Downstream-only conda deps (not used by skillmodels itself; required
+  # to run skane-struct-bw / health-cognition pipelines):
+  - deepdiff >=8.5.0  # health-cognition + skane: snapshot / diff utilities
+  - memray >=1.17.2  # health-cognition + skane: memory profiling
+  - statsmodels >=0.14.5  # health-cognition + skane: regression diagnostics
+  - tabulate >=0.9.0  # health-cognition + skane: table formatting in reports
+  - seaborn *  # health-cognition: figure styling
+  - pip
+  - pip:
+      # Skillmodels project deps (PyPI)
+      - dags>=0.5.1
+      - jax>=0.9
+      - jupyter-book>=2
+      - kaleido>=1.2
+      - numpy>=2.4
+      - pandas>=3
+      - plotly>=6.6
+      - pytask>=0.5.8
+      - pytask-parallel>=0.5.2
+      - pdbp
+      # Pinned to the optimagic branch the AF estimator relies on
+      # (`probability-allow-fixed-entries`). The PyPI release does not
+      # yet carry the required `FixedConstraintWithValue` semantics.
+      # yamllint disable-line rule:line-length
+      - optimagic @ git+https://github.com/optimagic-dev/optimagic.git@probability-allow-fixed-entries
+      # Downstream-only PyPI deps (not used by skillmodels itself):
+      - fides>=0.7.8  # health-cognition + skane: optimagic algorithm
+      - statadict>=1.1.0  # health-cognition + skane: Stata variable labels
+      # The library itself, from the af-estimator branch.
+      # yamllint disable-line rule:line-length
+      - skillmodels @ git+https://github.com/OpenSourceEconomics/skillmodels.git@af-estimator