S-update-project

.pre-commit-config.yaml

@@ -2,16 +2,22 @@ repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0
     hooks:
+    # File format checks
     -   id: check-yaml
-    -   id: end-of-file-fixer
+    -   id: check-toml
+    # Code quality checks
+    -   id: debug-statements
+    -   id: check-added-large-files
+    # File formatting fixes
+    -   id: mixed-line-ending
     -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+-   repo: https://github.com/psf/black
+    rev: 25.1.0
+    hooks:
+    -   id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    # Ruff version.
-    rev: v0.9.1
+    rev: v0.11.7
     hooks:
     -   id: ruff
         args: ["--fix", "--output-format=full"]
--   repo: https://github.com/psf/black
-    rev: 24.10.0
-    hooks:
-    -   id: black

doubleml/did/did_multi.py

@@ -47,7 +47,7 @@ class DoubleMLDIDMulti:
     ml_g : estimator implementing ``fit()`` and ``predict()``
         A machine learner implementing ``fit()`` and ``predict()`` methods (e.g.
         :py:class:`sklearn.ensemble.RandomForestRegressor`) for the nuisance function
-        :math:`g_0(0,X) = E[Y_{t_\text{eval}}-Y_{t_\text{pre}}|X, C__{t_\text{eval} + \\delta} = 1]`.
+        :math:`g_0(0,X) = E[Y_{t_{\\text{eval}}}-Y_{t_{\\text{pre}}}|X, C_{t_{\text{eval}} + \\delta} = 1]`.
         For a binary outcome variable :math:`Y` (with values 0 and 1), a classifier implementing ``fit()`` and
         ``predict_proba()`` can also be specified.
 

pyproject.toml

@@ -44,14 +44,14 @@ dev = [
     "pytest-cov>=6.0.0",
     "xgboost",
     "lightgbm",
-    "black>=24.3.0",
-    "ruff>=0.5.1",
-    "pre-commit>=4.0.1",
+    "black>=25.1.0",
+    "ruff>=0.11.1",
+    "pre-commit>=4.2.0",
 ]
 
 [tool.black]
 line-length = 127
-target-version = ['py39', 'py310', 'py311']
+target-version = ['py39', 'py310', 'py311', 'py312']
 preview = true
 exclude = '''
 /(