ISLP 0.7: ModelSpec from modelmatrix

CHANGELOG.md

@@ -2,6 +2,34 @@
 
 ## Unreleased
 
+`ModelSpec` has moved to its own package,
+[modelmatrix](https://github.com/intro-stat-learning/modelmatrix), which
+ISLP now depends on (`modelmatrix>=0.3`).
+
+### Breaking changes
+
+- **`ModelSpec`'s `intercept` defaults to `False`**, as 0.6.0 announced. Pass
+  `intercept=True` for statsmodels' models (`OLS`, `GLM`, ...) and for
+  `sklearn_sm` and `sklearn_selected` with them; most scikit-learn
+  estimators, and glmstar's, fit the intercept themselves. The
+  `FutureWarning` of 0.6.0 is gone.
+
+### Changes
+
+- `from ISLP.models import ModelSpec` and the other tools that build model
+  matrices are modelmatrix's, as are `ISLP.models.model_spec`,
+  `ISLP.models.columns` and `ISLP.transforms`, which now are modelmatrix's
+  modules, so imports from them keep working. `ISLP.models` also exports
+  modelmatrix's `wildcard` (R's `.`), `Contrast`, `Ordinal` and its
+  responses `Surv`, `Binomial`, `Multinomial` and `Outcome`.
+- ISLP's tests of `ModelSpec` moved to modelmatrix.
+
+### Bug fixes
+
+- Stepwise selection (`Stepwise`, `min_max`) counts and finds terms among
+  `ModelSpec.terms_`, the fitted terms, not `terms`: with a `wildcard`, a
+  single term standing for many, the terms were misaligned.
+
 ## 0.6.0 (2026-10-26)
 
 `ModelSpec` was audited before it moves to its own package in ISLP 0.7.

ISLP/models/__init__.py

@@ -2,21 +2,33 @@
 This package contains tools to specify, manipulate
 and select regression models.
 
+`ModelSpec` and the features of a model are those of the modelmatrix
+package, imported here so that `from ISLP.models import ModelSpec`
+keeps working.
 """
 import numpy as np, pandas as pd
 from io import StringIO
 
-from .model_spec import (ModelSpec,
+from modelmatrix import (ModelSpec,
                          Column,
                          Feature,
+                         Contrast,
+                         Ordinal,
                          poly,
                          ns,
                          bs,
                          derived_feature,
                          pca,
                          contrast,
                          ordinal,
-                         build_columns)
+                         build_columns,
+                         wildcard,
+                         Wildcard,
+                         Response,
+                         Surv,
+                         Binomial,
+                         Multinomial,
+                         Outcome)
 from .strategy import (min_max as min_max_strategy,
                        Stepwise)
 from .generic_selector import FeatureSelector