diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 26cacc45..135f066a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
     # File format checks
     -   id: check-yaml
@@ -13,11 +13,11 @@ repos:
     -   id: trailing-whitespace
     -   id: end-of-file-fixer
 -   repo: https://github.com/psf/black
-    rev: 25.1.0
+    rev: 25.9.0
     hooks:
     -   id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.3
+    rev: v0.14.4
     hooks:
-    -   id: ruff
+    -   id: ruff-check
         args: ["--fix", "--output-format=full"]
diff --git a/pyproject.toml b/pyproject.toml
index 06cb6502..3b6460c4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,6 +56,23 @@ Source = "https://github.com/DoubleML/doubleml-for-py"
 "Bug Tracker" = "https://github.com/DoubleML/doubleml-for-py/issues"
 
 [tool.pytest.ini_options]
+markers = [
+    "ci: mark a test as a continuous integration test which will be executed in github actions.",
+    "ci_rdd: mark a test as a continuous integration test which will be executed in github actions and is included in the rdd submodule."
+]
+filterwarnings = [
+    "ignore:.*A sharp RD design is being estimated, but the data indicate that the design is fuzzy.*:UserWarning",
+    "ignore:.*A learner ml_m has been provided for for a sharp design but will be ignored. A learner ml_m is not required for estimation.*:UserWarning",
+    "ignore:.*A learner ml_m has been provided for score = \"experimental\" but will be ignored.*:UserWarning",
+    "ignore:.*A learner ml_g has been provided for score = \"partialling out\" but will be ignored.*:UserWarning",
+    "ignore:.*Propensity predictions from learner RandomForestClassifier\\(n_estimators=10\\) for ml_m are close to zero or one \\(eps=1e-12\\).*:UserWarning",
+    "ignore:.*Returning pointwise confidence intervals for basis coefficients.*:UserWarning",
+    "ignore:.*Propensity score is close to 0 or 1. Trimming is at 0.01 and 0.99 is applied.*:UserWarning",
+    "ignore:.*Sensitivity analysis not implemented for callable scores.*:UserWarning",
+    "ignore:.*Subsample has not common support. Results are based on adjusted propensities.*:UserWarning",
+    "ignore:.*Treatment probability within bandwidth left from cutoff higher than right from cutoff.\\nTreatment assignment might be based on the wrong side of the cutoff.*:UserWarning",
+    "ignore:.*The estimated nu2 for d is not positive.*:UserWarning"
+]
 addopts = [
     "--doctest-modules",
     "--doctest-ignore-import-errors"
diff --git a/pytest.ini b/pytest.ini
deleted file mode 100644
index f7125f42..00000000
--- a/pytest.ini
+++ /dev/null
@@ -1,18 +0,0 @@
-# content of pytest.ini
-[pytest]
-markers =
-    ci: mark a test as a continuous integration test which will be executed in github actions.
-    ci_rdd: mark a test as a continuous integration test which will be executed in github actions and is included in the rdd submodule.
-
-filterwarnings =
-    ignore:.*A sharp RD design is being estimated, but the data indicate that the design is fuzzy.*:UserWarning
-    ignore:.*A learner ml_m has been provided for for a sharp design but will be ignored. A learner ml_m is not required for estimation.*:UserWarning
-    ignore:.*A learner ml_m has been provided for score = "experimental" but will be ignored.*:UserWarning
-    ignore:.*A learner ml_g has been provided for score = "partialling out" but will be ignored.*:UserWarning
-    ignore:.*Propensity predictions from learner RandomForestClassifier\(n_estimators=10\) for ml_m are close to zero or one \(eps=1e-12\).*:UserWarning
-    ignore:.*Returning pointwise confidence intervals for basis coefficients.*:UserWarning
-    ignore:.*Propensity score is close to 0 or 1. Trimming is at 0.01 and 0.99 is applied.*:UserWarning
-    ignore:.*Sensitivity analysis not implemented for callable scores.*:UserWarning
-    ignore:.*Subsample has not common support. Results are based on adjusted propensities.*:UserWarning
-    ignore:.*Treatment probability within bandwidth left from cutoff higher than right from cutoff.\nTreatment assignment might be based on the wrong side of the cutoff.*:UserWarning
-    ignore:.*The estimated nu2 for d is not positive.*:UserWarning