diff --git a/imblearn/combine/_smote_enn.py b/imblearn/combine/_smote_enn.py
index 1b0ffe0b8..d9d65c792 100644
--- a/imblearn/combine/_smote_enn.py
+++ b/imblearn/combine/_smote_enn.py
@@ -141,7 +141,6 @@ def _validate_estimator(self):
             self.smote_ = SMOTE(
                 sampling_strategy=self.sampling_strategy,
                 random_state=self.random_state,
-                n_jobs=self.n_jobs,
             )
 
         if self.enn is not None:
diff --git a/imblearn/combine/_smote_tomek.py b/imblearn/combine/_smote_tomek.py
index 94d7c4d01..a0c606dd0 100644
--- a/imblearn/combine/_smote_tomek.py
+++ b/imblearn/combine/_smote_tomek.py
@@ -140,7 +140,6 @@ def _validate_estimator(self):
             self.smote_ = SMOTE(
                 sampling_strategy=self.sampling_strategy,
                 random_state=self.random_state,
-                n_jobs=self.n_jobs,
             )
 
         if self.tomek is not None:
diff --git a/imblearn/combine/tests/test_smote_enn.py b/imblearn/combine/tests/test_smote_enn.py
index df72cc749..f6eb7b37c 100644
--- a/imblearn/combine/tests/test_smote_enn.py
+++ b/imblearn/combine/tests/test_smote_enn.py
@@ -146,12 +146,10 @@ def test_parallelisation():
     smt = SMOTEENN(random_state=RND_SEED)
     smt._validate_estimator()
     assert smt.n_jobs is None
-    assert smt.smote_.n_jobs is None
     assert smt.enn_.n_jobs is None
 
     # Check if job count is set
     smt = SMOTEENN(random_state=RND_SEED, n_jobs=8)
     smt._validate_estimator()
     assert smt.n_jobs == 8
-    assert smt.smote_.n_jobs == 8
     assert smt.enn_.n_jobs == 8
diff --git a/imblearn/combine/tests/test_smote_tomek.py b/imblearn/combine/tests/test_smote_tomek.py
index 2ca3e38c2..97795e4bc 100644
--- a/imblearn/combine/tests/test_smote_tomek.py
+++ b/imblearn/combine/tests/test_smote_tomek.py
@@ -156,12 +156,10 @@ def test_parallelisation():
     smt = SMOTETomek(random_state=RND_SEED)
     smt._validate_estimator()
     assert smt.n_jobs is None
-    assert smt.smote_.n_jobs is None
     assert smt.tomek_.n_jobs is None
 
     # Check if job count is set
     smt = SMOTETomek(random_state=RND_SEED, n_jobs=8)
     smt._validate_estimator()
     assert smt.n_jobs == 8
-    assert smt.smote_.n_jobs == 8
     assert smt.tomek_.n_jobs == 8
diff --git a/imblearn/ensemble/_bagging.py b/imblearn/ensemble/_bagging.py
index fd4c253aa..0cf7a4ba8 100644
--- a/imblearn/ensemble/_bagging.py
+++ b/imblearn/ensemble/_bagging.py
@@ -10,22 +10,17 @@
 import numpy as np
 from sklearn.base import clone
 from sklearn.ensemble import BaggingClassifier
-from sklearn.ensemble._bagging import _parallel_decision_function
-from sklearn.ensemble._base import _partition_estimators
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils._param_validation import HasMethods, Interval, StrOptions
 from sklearn.utils.fixes import parse_version
-from sklearn.utils.metaestimators import available_if
-from sklearn.utils.parallel import Parallel, delayed
-from sklearn.utils.validation import check_is_fitted
 
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
 from ..utils import Substitution, check_sampling_strategy, check_target_type
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
-from ..utils._sklearn_compat import _fit_context, sklearn_version, validate_data
-from ._common import _bagging_parameter_constraints, _estimator_has
+from ..utils._sklearn_compat import _fit_context, sklearn_version
+from ._common import _bagging_parameter_constraints
 
 
 @Substitution(
@@ -356,54 +351,6 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         # None.
         return super()._fit(X, y, self.max_samples)
 
-    # TODO: remove when minimum supported version of scikit-learn is 1.1
-    @available_if(_estimator_has("decision_function"))
-    def decision_function(self, X):
-        """Average of the decision functions of the base classifiers.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            The training input samples. Sparse matrices are accepted only if
-            they are supported by the base estimator.
-
-        Returns
-        -------
-        score : ndarray of shape (n_samples, k)
-            The decision function of the input samples. The columns correspond
-            to the classes in sorted order, as they appear in the attribute
-            ``classes_``. Regression and binary classification are special
-            cases with ``k == 1``, otherwise ``k==n_classes``.
-        """
-        check_is_fitted(self)
-
-        # Check data
-        X = validate_data(
-            self,
-            X=X,
-            accept_sparse=["csr", "csc"],
-            dtype=None,
-            ensure_all_finite=False,
-            reset=False,
-        )
-
-        # Parallel loop
-        n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs)
-
-        all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
-            delayed(_parallel_decision_function)(
-                self.estimators_[starts[i] : starts[i + 1]],
-                self.estimators_features_[starts[i] : starts[i + 1]],
-                X,
-            )
-            for i in range(n_jobs)
-        )
-
-        # Reduce
-        decisions = sum(all_decisions) / self.n_estimators
-
-        return decisions
-
     @property
     def base_estimator_(self):
         """Attribute for older sklearn version compatibility."""
diff --git a/imblearn/ensemble/_easy_ensemble.py b/imblearn/ensemble/_easy_ensemble.py
index 9d5f83ab9..29f0095f2 100644
--- a/imblearn/ensemble/_easy_ensemble.py
+++ b/imblearn/ensemble/_easy_ensemble.py
@@ -6,7 +6,6 @@
 
 import copy
 import numbers
-import warnings
 
 import numpy as np
 from sklearn.base import clone
@@ -248,20 +247,6 @@ def _validate_estimator(self, default=AdaBoostClassifier(algorithm="SAMME")):
         )
         self.estimator_ = Pipeline([("sampler", sampler), ("classifier", estimator)])
 
-    # TODO: remove when supporting scikit-learn>=1.2
-    @property
-    def n_features_(self):
-        """Number of features when ``fit`` is performed."""
-        warnings.warn(
-            (
-                "`n_features_` was deprecated in scikit-learn 1.0. This attribute will "
-                "not be accessible when the minimum supported version of scikit-learn "
-                "is 1.2."
-            ),
-            FutureWarning,
-        )
-        return self.n_features_in_
-
     @_fit_context(prefer_skip_nested_validation=False)
     def fit(self, X, y):
         """Build a Bagging ensemble of estimators from the training set (X, y).
diff --git a/imblearn/ensemble/_forest.py b/imblearn/ensemble/_forest.py
index 6f4e8247b..88b4c514d 100644
--- a/imblearn/ensemble/_forest.py
+++ b/imblearn/ensemble/_forest.py
@@ -437,10 +437,10 @@ def __init__(
         max_features="sqrt",
         max_leaf_nodes=None,
         min_impurity_decrease=0.0,
-        bootstrap="warn",
+        bootstrap=False,
         oob_score=False,
-        sampling_strategy="warn",
-        replacement="warn",
+        sampling_strategy="all",
+        replacement=True,
         n_jobs=None,
         random_state=None,
         verbose=0,
@@ -498,7 +498,7 @@ def _validate_estimator(self, default=DecisionTreeClassifier()):
 
         self.base_sampler_ = RandomUnderSampler(
             sampling_strategy=self._sampling_strategy,
-            replacement=self._replacement,
+            replacement=self.replacement,
         )
 
     def _make_sampler_estimator(self, random_state=None):
@@ -544,49 +544,6 @@ def fit(self, X, y, sample_weight=None):
             The fitted instance.
         """
         self._validate_params()
-        # TODO: remove in 0.13
-        if self.sampling_strategy == "warn":
-            warn(
-                (
-                    "The default of `sampling_strategy` will change from `'auto'` to"
-                    " `'all'` in version 0.13. This change will follow the"
-                    " implementation proposed in the original paper. Set to `'all'` to"
-                    " silence this warning and adopt the future behaviour."
-                ),
-                FutureWarning,
-            )
-            self._sampling_strategy = "auto"
-        else:
-            self._sampling_strategy = self.sampling_strategy
-
-        if self.replacement == "warn":
-            warn(
-                (
-                    "The default of `replacement` will change from `False` to `True` in"
-                    " version 0.13. This change will follow the implementation proposed"
-                    " in the original paper. Set to `True` to silence this warning and"
-                    " adopt the future behaviour."
-                ),
-                FutureWarning,
-            )
-            self._replacement = False
-        else:
-            self._replacement = self.replacement
-
-        if self.bootstrap == "warn":
-            warn(
-                (
-                    "The default of `bootstrap` will change from `True` to `False` in"
-                    " version 0.13. This change will follow the implementation proposed"
-                    " in the original paper. Set to `False` to silence this warning and"
-                    " adopt the future behaviour."
-                ),
-                FutureWarning,
-            )
-            self._bootstrap = True
-        else:
-            self._bootstrap = self.bootstrap
-
         # Validate or convert input data
         if issparse(y):
             raise ValueError("sparse multilabel-indicator for y is not supported.")
@@ -657,7 +614,7 @@ def fit(self, X, y, sample_weight=None):
         if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
             y_encoded = np.ascontiguousarray(y_encoded, dtype=DOUBLE)
 
-        if isinstance(self._sampling_strategy, dict):
+        if isinstance(self.sampling_strategy, dict):
             self._sampling_strategy = {
                 np.where(self.classes_[0] == key)[0][0]: value
                 for key, value in check_sampling_strategy(
@@ -667,7 +624,7 @@ def fit(self, X, y, sample_weight=None):
                 ).items()
             }
         else:
-            self._sampling_strategy = self._sampling_strategy
+            self._sampling_strategy = self.sampling_strategy
 
         if expanded_class_weight is not None:
             if sample_weight is not None:
@@ -683,7 +640,7 @@ def fit(self, X, y, sample_weight=None):
         # Check parameters
         self._validate_estimator()
 
-        if not self._bootstrap and self.oob_score:
+        if not self.bootstrap and self.oob_score:
             raise ValueError("Out of bag estimation only available if bootstrap=True")
 
         random_state = check_random_state(self.random_state)
@@ -735,7 +692,7 @@ def fit(self, X, y, sample_weight=None):
                 delayed(_local_parallel_build_trees)(
                     s,
                     t,
-                    self._bootstrap,
+                    self.bootstrap,
                     X,
                     y_encoded,
                     sample_weight,
diff --git a/imblearn/ensemble/tests/test_forest.py b/imblearn/ensemble/tests/test_forest.py
index 2653512c2..f9b059ef8 100644
--- a/imblearn/ensemble/tests/test_forest.py
+++ b/imblearn/ensemble/tests/test_forest.py
@@ -217,24 +217,6 @@ def test_balanced_random_forest_oob_binomial(ratio):
     assert np.abs(erf.oob_score_ - 0.5) < 0.1
 
 
-# TODO: remove in 0.13
-def test_balanced_random_forest_change_behaviour(imbalanced_dataset):
-    """Check that we raise a change of behaviour for the parameters `sampling_strategy`
-    and `replacement`.
-    """
-    estimator = BalancedRandomForestClassifier(sampling_strategy="all", bootstrap=False)
-    with pytest.warns(FutureWarning, match="The default of `replacement`"):
-        estimator.fit(*imbalanced_dataset)
-    estimator = BalancedRandomForestClassifier(replacement=True, bootstrap=False)
-    with pytest.warns(FutureWarning, match="The default of `sampling_strategy`"):
-        estimator.fit(*imbalanced_dataset)
-    estimator = BalancedRandomForestClassifier(
-        sampling_strategy="all", replacement=True
-    )
-    with pytest.warns(FutureWarning, match="The default of `bootstrap`"):
-        estimator.fit(*imbalanced_dataset)
-
-
 @pytest.mark.skipif(
     parse_version(sklearn_version.base_version) < parse_version("1.4"),
     reason="scikit-learn should be >= 1.4",
diff --git a/imblearn/over_sampling/_adasyn.py b/imblearn/over_sampling/_adasyn.py
index c94494443..a0a6d128a 100644
--- a/imblearn/over_sampling/_adasyn.py
+++ b/imblearn/over_sampling/_adasyn.py
@@ -5,7 +5,6 @@
 # License: MIT
 
 import numbers
-import warnings
 
 import numpy as np
 from scipy import sparse
@@ -13,13 +12,12 @@
 from sklearn.utils._param_validation import HasMethods, Interval
 
 from ..utils import Substitution, check_neighbors_object
-from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
+from ..utils._docstring import _random_state_docstring
 from .base import BaseOverSampler
 
 
 @Substitution(
     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
-    n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
 class ADASYN(BaseOverSampler):
@@ -50,14 +48,6 @@ class ADASYN(BaseOverSampler):
           :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to
           any compatible class.
 
-    {n_jobs}
-
-        .. deprecated:: 0.10
-           `n_jobs` has been deprecated in 0.10 and will be removed in 0.12.
-           It was previously used to set `n_jobs` of nearest neighbors
-           algorithm. From now on, you can pass an estimator where `n_jobs` is
-           already set instead.
-
     Attributes
     ----------
     sampling_strategy_ : dict
@@ -128,7 +118,6 @@ class ADASYN(BaseOverSampler):
             Interval(numbers.Integral, 1, None, closed="left"),
             HasMethods(["kneighbors", "kneighbors_graph"]),
         ],
-        "n_jobs": [numbers.Integral, None],
     }
 
     def __init__(
@@ -137,12 +126,10 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         n_neighbors=5,
-        n_jobs=None,
     ):
         super().__init__(sampling_strategy=sampling_strategy)
         self.random_state = random_state
         self.n_neighbors = n_neighbors
-        self.n_jobs = n_jobs
 
     def _validate_estimator(self):
         """Create the necessary objects for ADASYN"""
@@ -151,17 +138,6 @@ def _validate_estimator(self):
         )
 
     def _fit_resample(self, X, y):
-        # FIXME: to be removed in 0.12
-        if self.n_jobs is not None:
-            warnings.warn(
-                (
-                    "The parameter `n_jobs` has been deprecated in 0.10 and will be"
-                    " removed in 0.12. You can pass an nearest neighbors estimator"
-                    " where `n_jobs` is already set instead."
-                ),
-                FutureWarning,
-            )
-
         self._validate_estimator()
         random_state = check_random_state(self.random_state)
 
diff --git a/imblearn/over_sampling/_smote/base.py b/imblearn/over_sampling/_smote/base.py
index c0be9e47b..7a6abbd2d 100644
--- a/imblearn/over_sampling/_smote/base.py
+++ b/imblearn/over_sampling/_smote/base.py
@@ -29,7 +29,7 @@
 
 from ...metrics.pairwise import ValueDifferenceMetric
 from ...utils import Substitution, check_neighbors_object, check_target_type
-from ...utils._docstring import _n_jobs_docstring, _random_state_docstring
+from ...utils._docstring import _random_state_docstring
 from ...utils._sklearn_compat import _get_column_indices, _is_pandas_df, validate_data
 from ...utils._validation import _check_X
 from ..base import BaseOverSampler
@@ -44,7 +44,6 @@ class BaseSMOTE(BaseOverSampler):
             Interval(numbers.Integral, 1, None, closed="left"),
             HasMethods(["kneighbors", "kneighbors_graph"]),
         ],
-        "n_jobs": [numbers.Integral, None],
     }
 
     def __init__(
@@ -52,12 +51,10 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         k_neighbors=5,
-        n_jobs=None,
     ):
         super().__init__(sampling_strategy=sampling_strategy)
         self.random_state = random_state
         self.k_neighbors = k_neighbors
-        self.n_jobs = n_jobs
 
     def _validate_estimator(self):
         """Check the NN estimators shared across the different SMOTE
@@ -238,7 +235,6 @@ def _in_danger_noise(self, nn_estimator, samples, target_class, y, kind="danger"
 
 @Substitution(
     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
-    n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
 class SMOTE(BaseSMOTE):
@@ -268,14 +264,6 @@ class SMOTE(BaseSMOTE):
           :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to
           any compatible class.
 
-    {n_jobs}
-
-        .. deprecated:: 0.10
-           `n_jobs` has been deprecated in 0.10 and will be removed in 0.12.
-           It was previously used to set `n_jobs` of nearest neighbors
-           algorithm. From now on, you can pass an estimator where `n_jobs` is
-           already set instead.
-
     Attributes
     ----------
     sampling_strategy_ : dict
@@ -348,27 +336,14 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         k_neighbors=5,
-        n_jobs=None,
     ):
         super().__init__(
             sampling_strategy=sampling_strategy,
             random_state=random_state,
             k_neighbors=k_neighbors,
-            n_jobs=n_jobs,
         )
 
     def _fit_resample(self, X, y):
-        # FIXME: to be removed in 0.12
-        if self.n_jobs is not None:
-            warnings.warn(
-                (
-                    "The parameter `n_jobs` has been deprecated in 0.10 and will be"
-                    " removed in 0.12. You can pass an nearest neighbors estimator"
-                    " where `n_jobs` is already set instead."
-                ),
-                FutureWarning,
-            )
-
         self._validate_estimator()
 
         X_resampled = [X.copy()]
@@ -399,7 +374,6 @@ def _fit_resample(self, X, y):
 
 @Substitution(
     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
-    n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
 class SMOTENC(SMOTE):
@@ -451,14 +425,6 @@ class SMOTENC(SMOTE):
           :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to
           any compatible class.
 
-    {n_jobs}
-
-        .. deprecated:: 0.10
-           `n_jobs` has been deprecated in 0.10 and will be removed in 0.12.
-           It was previously used to set `n_jobs` of nearest neighbors
-           algorithm. From now on, you can pass an estimator where `n_jobs` is
-           already set instead.
-
     Attributes
     ----------
     sampling_strategy_ : dict
@@ -469,13 +435,6 @@ class SMOTENC(SMOTE):
     nn_k_ : estimator object
         Validated k-nearest neighbours created from the `k_neighbors` parameter.
 
-    ohe_ : :class:`~sklearn.preprocessing.OneHotEncoder`
-        The one-hot encoder used to encode the categorical features.
-
-        .. deprecated:: 0.11
-           `ohe_` is deprecated in 0.11 and will be removed in 0.13. Use
-           `categorical_encoder_` instead.
-
     categorical_encoder_ : estimator
         The encoder used to encode the categorical features.
 
@@ -577,13 +536,11 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         k_neighbors=5,
-        n_jobs=None,
     ):
         super().__init__(
             sampling_strategy=sampling_strategy,
             random_state=random_state,
             k_neighbors=k_neighbors,
-            n_jobs=n_jobs,
         )
         self.categorical_features = categorical_features
         self.categorical_encoder = categorical_encoder
@@ -634,17 +591,6 @@ def _validate_estimator(self):
             )
 
     def _fit_resample(self, X, y):
-        # FIXME: to be removed in 0.12
-        if self.n_jobs is not None:
-            warnings.warn(
-                (
-                    "The parameter `n_jobs` has been deprecated in 0.10 and will be"
-                    " removed in 0.12. You can pass an nearest neighbors estimator"
-                    " where `n_jobs` is already set instead."
-                ),
-                FutureWarning,
-            )
-
         self.n_features_ = _num_features(X)
         self._validate_column_types(X)
         self._validate_estimator()
@@ -801,18 +747,6 @@ def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps, y_type, y=Non
 
         return X_new
 
-    @property
-    def ohe_(self):
-        """One-hot encoder used to encode the categorical features."""
-        warnings.warn(
-            (
-                "'ohe_' attribute has been deprecated in 0.11 and will be removed "
-                "in 0.13. Use 'categorical_encoder_' instead."
-            ),
-            FutureWarning,
-        )
-        return self.categorical_encoder_
-
     def _more_tags(self):
         return {"X_types": ["2darray", "dataframe", "string"]}
 
@@ -825,7 +759,6 @@ def __sklearn_tags__(self):
 
 @Substitution(
     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
-    n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
 class SMOTEN(SMOTE):
@@ -861,14 +794,6 @@ class SMOTEN(SMOTE):
           :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to
           any compatible class.
 
-    {n_jobs}
-
-        .. deprecated:: 0.10
-           `n_jobs` has been deprecated in 0.10 and will be removed in 0.12.
-           It was previously used to set `n_jobs` of nearest neighbors
-           algorithm. From now on, you can pass an estimator where `n_jobs` is
-           already set instead.
-
     Attributes
     ----------
     categorical_encoder_ : estimator
@@ -951,13 +876,11 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         k_neighbors=5,
-        n_jobs=None,
     ):
         super().__init__(
             sampling_strategy=sampling_strategy,
             random_state=random_state,
             k_neighbors=k_neighbors,
-            n_jobs=n_jobs,
         )
         self.categorical_encoder = categorical_encoder
 
@@ -996,17 +919,6 @@ def _make_samples(self, X_class, klass, y_dtype, nn_indices, n_samples):
         return X_new, y_new
 
     def _fit_resample(self, X, y):
-        # FIXME: to be removed in 0.12
-        if self.n_jobs is not None:
-            warnings.warn(
-                (
-                    "The parameter `n_jobs` has been deprecated in 0.10 and will be"
-                    " removed in 0.12. You can pass an nearest neighbors estimator"
-                    " where `n_jobs` is already set instead."
-                ),
-                FutureWarning,
-            )
-
         if sparse.issparse(X):
             X_sparse_format = X.format
             X = X.toarray()
diff --git a/imblearn/over_sampling/_smote/cluster.py b/imblearn/over_sampling/_smote/cluster.py
index a1959df50..59a28c635 100644
--- a/imblearn/over_sampling/_smote/cluster.py
+++ b/imblearn/over_sampling/_smote/cluster.py
@@ -155,6 +155,7 @@ class KMeansSMOTE(BaseSMOTE):
         ],
         "cluster_balance_threshold": [StrOptions({"auto"}), numbers.Real],
         "density_exponent": [StrOptions({"auto"}), numbers.Real],
+        "n_jobs": [numbers.Integral, None],
     }
 
     def __init__(
@@ -172,11 +173,11 @@ def __init__(
             sampling_strategy=sampling_strategy,
             random_state=random_state,
             k_neighbors=k_neighbors,
-            n_jobs=n_jobs,
         )
         self.kmeans_estimator = kmeans_estimator
         self.cluster_balance_threshold = cluster_balance_threshold
         self.density_exponent = density_exponent
+        self.n_jobs = n_jobs
 
     def _validate_estimator(self):
         super()._validate_estimator()
diff --git a/imblearn/over_sampling/_smote/filter.py b/imblearn/over_sampling/_smote/filter.py
index 74782e67c..126ba533e 100644
--- a/imblearn/over_sampling/_smote/filter.py
+++ b/imblearn/over_sampling/_smote/filter.py
@@ -7,7 +7,6 @@
 # License: MIT
 
 import numbers
-import warnings
 
 import numpy as np
 from scipy import sparse
@@ -17,14 +16,13 @@
 from sklearn.utils._param_validation import HasMethods, Interval, StrOptions
 
 from ...utils import Substitution, check_neighbors_object
-from ...utils._docstring import _n_jobs_docstring, _random_state_docstring
+from ...utils._docstring import _random_state_docstring
 from ..base import BaseOverSampler
 from .base import BaseSMOTE
 
 
 @Substitution(
     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
-    n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
 class BorderlineSMOTE(BaseSMOTE):
@@ -57,14 +55,6 @@ class BorderlineSMOTE(BaseSMOTE):
           :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to
           any compatible class.
 
-    {n_jobs}
-
-        .. deprecated:: 0.10
-           `n_jobs` has been deprecated in 0.10 and will be removed in 0.12.
-           It was previously used to set `n_jobs` of nearest neighbors
-           algorithm. From now on, you can pass an estimator where `n_jobs` is
-           already set instead.
-
     m_neighbors : int or object, default=10
         The nearest neighbors used to determine if a minority sample is in
         "danger". You can pass:
@@ -172,7 +162,6 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         k_neighbors=5,
-        n_jobs=None,
         m_neighbors=10,
         kind="borderline-1",
     ):
@@ -180,7 +169,6 @@ def __init__(
             sampling_strategy=sampling_strategy,
             random_state=random_state,
             k_neighbors=k_neighbors,
-            n_jobs=n_jobs,
         )
         self.m_neighbors = m_neighbors
         self.kind = kind
@@ -192,17 +180,6 @@ def _validate_estimator(self):
         )
 
     def _fit_resample(self, X, y):
-        # FIXME: to be removed in 0.12
-        if self.n_jobs is not None:
-            warnings.warn(
-                (
-                    "The parameter `n_jobs` has been deprecated in 0.10 and will be"
-                    " removed in 0.12. You can pass an nearest neighbors estimator"
-                    " where `n_jobs` is already set instead."
-                ),
-                FutureWarning,
-            )
-
         self._validate_estimator()
 
         X_resampled = X.copy()
@@ -253,7 +230,6 @@ def _fit_resample(self, X, y):
 
 @Substitution(
     sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
-    n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
 class SVMSMOTE(BaseSMOTE):
@@ -285,14 +261,6 @@ class SVMSMOTE(BaseSMOTE):
           :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to
           any compatible class.
 
-    {n_jobs}
-
-        .. deprecated:: 0.10
-           `n_jobs` has been deprecated in 0.10 and will be removed in 0.12.
-           It was previously used to set `n_jobs` of nearest neighbors
-           algorithm. From now on, you can pass an estimator where `n_jobs` is
-           already set instead.
-
     m_neighbors : int or object, default=10
         The nearest neighbors used to determine if a minority sample is in
         "danger". You can pass:
@@ -407,7 +375,6 @@ def __init__(
         sampling_strategy="auto",
         random_state=None,
         k_neighbors=5,
-        n_jobs=None,
         m_neighbors=10,
         svm_estimator=None,
         out_step=0.5,
@@ -416,7 +383,6 @@ def __init__(
             sampling_strategy=sampling_strategy,
             random_state=random_state,
             k_neighbors=k_neighbors,
-            n_jobs=n_jobs,
         )
         self.m_neighbors = m_neighbors
         self.svm_estimator = svm_estimator
@@ -434,17 +400,6 @@ def _validate_estimator(self):
             self.svm_estimator_ = clone(self.svm_estimator)
 
     def _fit_resample(self, X, y):
-        # FIXME: to be removed in 0.12
-        if self.n_jobs is not None:
-            warnings.warn(
-                (
-                    "The parameter `n_jobs` has been deprecated in 0.10 and will be"
-                    " removed in 0.12. You can pass an nearest neighbors estimator"
-                    " where `n_jobs` is already set instead."
-                ),
-                FutureWarning,
-            )
-
         self._validate_estimator()
         random_state = check_random_state(self.random_state)
         X_resampled = X.copy()
diff --git a/imblearn/over_sampling/_smote/tests/test_smote_nc.py b/imblearn/over_sampling/_smote/tests/test_smote_nc.py
index d17873e73..2259acd3c 100644
--- a/imblearn/over_sampling/_smote/tests/test_smote_nc.py
+++ b/imblearn/over_sampling/_smote/tests/test_smote_nc.py
@@ -277,17 +277,6 @@ def test_smotenc_categorical_encoder():
     assert getattr(smote.categorical_encoder_, "sparse_output") is False
 
 
-# TODO(0.13): remove this test
-def test_smotenc_deprecation_ohe_():
-    """Check that we raise a deprecation warning when using `ohe_`."""
-    X, y, categorical_features = data_heterogneous_unordered()
-    smote = SMOTENC(categorical_features=categorical_features, random_state=0)
-    smote.fit_resample(X, y)
-
-    with pytest.warns(FutureWarning, match="'ohe_' attribute has been deprecated"):
-        smote.ohe_
-
-
 def test_smotenc_bool_categorical():
     """Check that we don't try to early convert the full input data to numeric when
     handling a pandas dataframe.
diff --git a/imblearn/over_sampling/tests/test_common.py b/imblearn/over_sampling/tests/test_common.py
index cdd85c151..3e82dbb3c 100644
--- a/imblearn/over_sampling/tests/test_common.py
+++ b/imblearn/over_sampling/tests/test_common.py
@@ -123,23 +123,3 @@ def test_numerical_smote_extra_custom_nn(numerical_data, smote):
 
     assert X_res.shape == (120, 2)
     assert Counter(y_res) == {0: 60, 1: 60}
-
-
-# FIXME: to be removed in 0.12
-@pytest.mark.parametrize(
-    "sampler",
-    [
-        ADASYN(random_state=0),
-        BorderlineSMOTE(random_state=0),
-        SMOTE(random_state=0),
-        SMOTEN(random_state=0),
-        SMOTENC([0], random_state=0),
-        SVMSMOTE(random_state=0),
-    ],
-)
-def test_n_jobs_deprecation_warning(numerical_data, sampler):
-    X, y = numerical_data
-    sampler.set_params(n_jobs=2)
-    warning_msg = "The parameter `n_jobs` has been deprecated"
-    with pytest.warns(FutureWarning, match=warning_msg):
-        sampler.fit_resample(X, y)
diff --git a/imblearn/over_sampling/tests/test_random_over_sampler.py b/imblearn/over_sampling/tests/test_random_over_sampler.py
index efa40c855..e3a6c87d7 100644
--- a/imblearn/over_sampling/tests/test_random_over_sampler.py
+++ b/imblearn/over_sampling/tests/test_random_over_sampler.py
@@ -77,8 +77,7 @@ def test_ros_fit_resample(X_type, data, params):
 
     if X_type == "dataframe":
         assert hasattr(X_resampled, "loc")
-        # FIXME: we should use to_numpy with pandas >= 0.25
-        X_resampled = X_resampled.values
+        X_resampled = X_resampled.to_numpy()
 
     assert_allclose(X_resampled, X_gt)
     assert_array_equal(y_resampled, y_gt)
diff --git a/imblearn/pipeline.py b/imblearn/pipeline.py
index 7972b9b30..0ef7288b8 100644
--- a/imblearn/pipeline.py
+++ b/imblearn/pipeline.py
@@ -655,7 +655,7 @@ def predict(self, X, **params):
         y_pred : ndarray
             Result of calling `predict` on the final estimator.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             Xt = X
 
@@ -849,7 +849,7 @@ def predict_proba(self, X, **params):
         y_proba : ndarray of shape (n_samples, n_classes)
             Result of calling `predict_proba` on the final estimator.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             Xt = X
 
@@ -896,7 +896,7 @@ def decision_function(self, X, **params):
         y_score : ndarray of shape (n_samples, n_classes)
             Result of calling `decision_function` on the final estimator.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             _raise_for_params(params, self, "decision_function")
 
@@ -934,7 +934,7 @@ def score_samples(self, X):
         y_score : ndarray of shape (n_samples,)
             Result of calling `score_samples` on the final estimator.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             Xt = X
             for _, _, transformer in self._iter(with_final=False):
@@ -983,7 +983,7 @@ def predict_log_proba(self, X, **params):
         y_log_proba : ndarray of shape (n_samples, n_classes)
             Result of calling `predict_log_proba` on the final estimator.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             Xt = X
 
@@ -1038,7 +1038,7 @@ def transform(self, X, **params):
         Xt : ndarray of shape (n_samples, n_transformed_features)
             Transformed data.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             _raise_for_params(params, self, "transform")
 
@@ -1083,7 +1083,7 @@ def inverse_transform(self, Xt, **params):
             Inverse transformed data, that is, data in the original feature
             space.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             _raise_for_params(params, self, "inverse_transform")
 
@@ -1134,7 +1134,7 @@ def score(self, X, y=None, sample_weight=None, **params):
         score : float
             Result of calling `score` on the final estimator.
         """
-        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        # TODO(0.15): Remove the context manager and use check_is_fitted(self)
         with _raise_or_warn_if_not_fitted(self):
             Xt = X
             if not _routing_enabled():
diff --git a/imblearn/under_sampling/_prototype_selection/tests/test_random_under_sampler.py b/imblearn/under_sampling/_prototype_selection/tests/test_random_under_sampler.py
index f4e927902..7bf0edbe6 100644
--- a/imblearn/under_sampling/_prototype_selection/tests/test_random_under_sampler.py
+++ b/imblearn/under_sampling/_prototype_selection/tests/test_random_under_sampler.py
@@ -55,8 +55,7 @@ def test_rus_fit_resample(as_frame):
 
     if as_frame:
         assert hasattr(X_resampled, "loc")
-        # FIXME: we should use to_numpy with pandas >= 0.25
-        X_resampled = X_resampled.values
+        X_resampled = X_resampled.to_numpy()
 
     assert_array_equal(X_resampled, X_gt)
     assert_array_equal(y_resampled, y_gt)
diff --git a/imblearn/utils/estimator_checks.py b/imblearn/utils/estimator_checks.py
index 01d0139ad..d5f9cd80d 100644
--- a/imblearn/utils/estimator_checks.py
+++ b/imblearn/utils/estimator_checks.py
@@ -71,10 +71,6 @@ def _set_checking_parameters(estimator):
         )
     if name == "KMeansSMOTE":
         estimator.set_params(kmeans_estimator=12)
-    if name == "BalancedRandomForestClassifier":
-        # TODO: remove in 0.13
-        # future default in 0.13
-        estimator.set_params(replacement=True, sampling_strategy="all", bootstrap=False)
 
 
 def _yield_sampler_checks(sampler):
@@ -491,9 +487,8 @@ def check_samplers_pandas_sparse(name, sampler_orig):
     assert X_df.columns.tolist() == X_res_df.columns.tolist()
     assert y_s.name == y_res_s.name
 
-    # FIXME: we should use to_numpy with pandas >= 0.25
-    assert_allclose(X_res_df.values, X_res)
-    assert_allclose(y_res_s.values, y_res)
+    assert_allclose(X_res_df.to_numpy(), X_res)
+    assert_allclose(y_res_s.to_numpy(), y_res)
 
 
 def check_samplers_pandas(name, sampler_orig):
@@ -523,10 +518,9 @@ def check_samplers_pandas(name, sampler_orig):
     assert y_df.columns.tolist() == y_res_df.columns.tolist()
     assert y_s.name == y_res_s.name
 
-    # FIXME: we should use to_numpy with pandas >= 0.25
-    assert_allclose(X_res_df.values, X_res)
-    assert_allclose(y_res_df.values.ravel(), y_res)
-    assert_allclose(y_res_s.values, y_res)
+    assert_allclose(X_res_df.to_numpy(), X_res)
+    assert_allclose(y_res_df.to_numpy().ravel(), y_res)
+    assert_allclose(y_res_s.to_numpy(), y_res)
 
 
 def check_samplers_list(name, sampler_orig):
diff --git a/pixi.lock b/pixi.lock
index 864c5b2fd..9822b8bbb 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -93,7 +93,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-17_osx64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-17_osx64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-19.1.5-hf95d169_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-19.1.6-hf95d169_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.4.2-h0d85af4_5.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-11_3_0_h97931a8_32.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-12.2.0-he409387_32.conda
@@ -102,7 +102,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.23-openmp_h429af6e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.47.2-hdb6dae5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.1-hd23fc13_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-19.1.5-ha54dae1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-19.1.6-ha54dae1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.5-hf036a51_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.24.3-py310h7451ae0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.4.0-hd471939_0.conda
@@ -151,7 +151,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-20_osxarm64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-20_osxarm64_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.5-ha82da77_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.6-ha82da77_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.4.2-h3422bc3_5.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-5.0.0-13_2_0_hd922786_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-13.2.0-hf226fd6_3.conda
@@ -160,7 +160,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.25-openmp_h6c19121_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.47.2-h3f77e49_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.1-h8359307_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.5-hdb05f8b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-19.1.6-hdb05f8b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h7bae524_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.24.3-py310haa1e00c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.4.0-h39f12f2_0.conda
@@ -208,12 +208,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.1-h57928b3_1083.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.1-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-25_win64_mkl.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-25_win64_mkl.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-26_win64_mkl.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-26_win64_mkl.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.11.2-default_hc8275d1_1000.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/libiconv-1.17-hcfcfb64_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-25_win64_mkl.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-26_win64_mkl.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/liblzma-5.6.3-h2466b09_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.47.2-h67fdade_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.5-he286e8c_1.conda
@@ -223,7 +223,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2024.2.2-h66d3029_14.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2024.2.2-h66d3029_15.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.24.3-py310hd02465a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.4.0-h2466b09_0.conda
@@ -279,8 +279,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dm-tree-0.1.8-py310hbae6a42_6.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/h5py-3.12.1-nompi_py310h60e0fe6_102.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.3-nompi_h2d575fe_108.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/h5py-3.12.1-nompi_py310hacc6608_103.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.4-nompi_h2d575fe_105.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
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+  sha256: 4e860b60c06be04f2c37c45def870e4ea5268f568547b80a8f69ad6ecddb6f31
+  md5: 2f03da7a6d52d98bbea1f7390d6997bf
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   - libopenblas >=0.3.28,<0.3.29.0a0
   - libopenblas >=0.3.28,<1.0a0
   constrains:
-  - liblapack 3.9.0 25_osx64_openblas
-  - liblapacke 3.9.0 25_osx64_openblas
+  - libcblas 3.9.0 26_osx64_openblas
+  - liblapack 3.9.0 26_osx64_openblas
   - blas * openblas
-  - libcblas 3.9.0 25_osx64_openblas
+  - liblapacke 3.9.0 26_osx64_openblas
   arch: x86_64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15952
-  timestamp: 1729643159199
+  size: 16611
+  timestamp: 1734432938741
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-20_osxarm64_openblas.conda
   build_number: 20
   sha256: 5b5b8394352c8ca06b15dcc9319d0af3e9f1dc03fc0a6f6deef05d664d6b763a
@@ -8793,43 +8796,43 @@ packages:
   purls: []
   size: 14722
   timestamp: 1700568881837
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-25_osxarm64_openblas.conda
-  build_number: 25
-  sha256: f1fb9a11af0b2878bd8804b4c77d3733c40076218bcbdb35f575b1c0c9fddf11
-  md5: f8cf4d920ff36ce471619010eff59cac
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-26_osxarm64_openblas.conda
+  build_number: 26
+  sha256: 597f9c3779caa979c8c6abbb3ba8c7191b84e1a910d6b0d10e5faf35284c450c
+  md5: 21be102c9ae80a67ba7de23b129aa7f6
   depends:
   - libopenblas >=0.3.28,<0.3.29.0a0
   - libopenblas >=0.3.28,<1.0a0
   constrains:
+  - liblapack 3.9.0 26_osxarm64_openblas
+  - liblapacke 3.9.0 26_osxarm64_openblas
+  - libcblas 3.9.0 26_osxarm64_openblas
   - blas * openblas
-  - liblapack 3.9.0 25_osxarm64_openblas
-  - liblapacke 3.9.0 25_osxarm64_openblas
-  - libcblas 3.9.0 25_osxarm64_openblas
   arch: arm64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15913
-  timestamp: 1729643265495
-- conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-25_win64_mkl.conda
-  build_number: 25
-  sha256: 5468bb91c44b41ce060bbd997c797b2f91e2b7ce91a7cbf4ddf7e7b734a8dc98
-  md5: 499208e81242efb6e5abc7366c91c816
+  size: 16714
+  timestamp: 1734433054681
+- conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-26_win64_mkl.conda
+  build_number: 26
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+  md5: ecfe732dbad1be001826fdb7e5e891b5
   depends:
-  - mkl 2024.2.2 h66d3029_14
+  - mkl 2024.2.2 h66d3029_15
   constrains:
+  - liblapacke 3.9.0 26_win64_mkl
+  - liblapack 3.9.0 26_win64_mkl
   - blas * mkl
-  - libcblas 3.9.0 25_win64_mkl
-  - liblapack 3.9.0 25_win64_mkl
-  - liblapacke 3.9.0 25_win64_mkl
+  - libcblas 3.9.0 26_win64_mkl
   arch: x86_64
   platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 3736641
-  timestamp: 1729643534444
+  size: 3733122
+  timestamp: 1734432745507
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda
   sha256: d9db2de60ea917298e658143354a530e9ca5f9c63471c65cf47ab39fd2f429e3
   md5: 41b599ed2b02abcfdd84302bff174b23
@@ -8964,23 +8967,23 @@ packages:
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   size: 14383
   timestamp: 1700568410580
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-25_linux64_openblas.conda
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-  sha256: ab87b0477078837c91d9cda62a9faca18fba7c57cc77aa779ae24b3ac783b5dd
-  md5: 5dbd1b0fc0d01ec5e0e1fbe667281a11
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-26_linux64_openblas.conda
+  build_number: 26
+  sha256: 9c74e536c9bc868e356ffd43f81c2cb398aec84b40fcadc312315b164a5500ee
+  md5: ebcc5f37a435aa3c19640533c82f8d76
   depends:
-  - libblas 3.9.0 25_linux64_openblas
+  - libblas 3.9.0 26_linux64_openblas
   constrains:
-  - liblapack 3.9.0 25_linux64_openblas
+  - liblapack 3.9.0 26_linux64_openblas
+  - liblapacke 3.9.0 26_linux64_openblas
   - blas * openblas
-  - liblapacke 3.9.0 25_linux64_openblas
   arch: x86_64
   platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15613
-  timestamp: 1729642905619
+  size: 16336
+  timestamp: 1734432570482
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-17_osx64_openblas.conda
   build_number: 17
   sha256: 5c85941b55a8e897e11188ab66900eb3d83c87f6bdd0b88856733f8510fa4c91
@@ -8998,23 +9001,23 @@ packages:
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   timestamp: 1685931005294
-- conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-25_osx64_openblas.conda
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-  md5: ab304b75ea67f850cf7adf9156e3f62f
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-26_osx64_openblas.conda
+  build_number: 26
+  sha256: 4d5dd9aeca2fa37f01d6c0bdbafba0e4f8b6601758239fa85d0640d012a151d6
+  md5: 8726a2949c303b23da89be658a19675c
   depends:
-  - libblas 3.9.0 25_osx64_openblas
+  - libblas 3.9.0 26_osx64_openblas
   constrains:
-  - liblapack 3.9.0 25_osx64_openblas
-  - liblapacke 3.9.0 25_osx64_openblas
+  - liblapack 3.9.0 26_osx64_openblas
   - blas * openblas
+  - liblapacke 3.9.0 26_osx64_openblas
   arch: x86_64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15842
-  timestamp: 1729643166929
+  size: 16579
+  timestamp: 1734432954376
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-20_osxarm64_openblas.conda
   build_number: 20
   sha256: d3a74638f60e034202e373cf2950c69a8d831190d497881d13cbf789434d2489
@@ -9032,70 +9035,70 @@ packages:
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   size: 14642
   timestamp: 1700568912840
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-25_osxarm64_openblas.conda
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-  md5: 4df0fae81f0b5bf47d48c882b086da11
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-26_osxarm64_openblas.conda
+  build_number: 26
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+  md5: a0e9980fe12d42f6d0c0ec009f67e948
   depends:
-  - libblas 3.9.0 25_osxarm64_openblas
+  - libblas 3.9.0 26_osxarm64_openblas
   constrains:
+  - liblapack 3.9.0 26_osxarm64_openblas
+  - liblapacke 3.9.0 26_osxarm64_openblas
   - blas * openblas
-  - liblapack 3.9.0 25_osxarm64_openblas
-  - liblapacke 3.9.0 25_osxarm64_openblas
   arch: arm64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-25_win64_mkl.conda
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-  md5: 3ed189ba03a9888a8013aaee0d67c49d
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+  timestamp: 1734433061517
+- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-26_win64_mkl.conda
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   depends:
-  - libblas 3.9.0 25_win64_mkl
+  - libblas 3.9.0 26_win64_mkl
   constrains:
+  - liblapacke 3.9.0 26_win64_mkl
+  - liblapack 3.9.0 26_win64_mkl
   - blas * mkl
-  - liblapack 3.9.0 25_win64_mkl
-  - liblapacke 3.9.0 25_win64_mkl
   arch: x86_64
   platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp19.1-19.1.5-default_hb5137d0_0.conda
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-  md5: ec8649c89988d8a443c252c20f259b72
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp19.1-19.1.6-default_hb5137d0_0.conda
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+  md5: 9caebd39281536bf6bcb32f665dd4fbf
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  - libllvm19 >=19.1.5,<19.2.0a0
+  - libllvm19 >=19.1.6,<19.2.0a0
   - libstdcxx >=13
   arch: x86_64
   platform: linux
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-19.1.5-default_h9c6a7e4_0.conda
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-  md5: a3a5997b6b47373f0c1608d8503eb4e6
+  size: 20531147
+  timestamp: 1734506894098
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-19.1.6-default_h9c6a7e4_0.conda
+  sha256: 54a7fabfba7dee2caebe5e6a7538e3aba0a8f4c11e7366f65592aee4fdaa7519
+  md5: e1d2936c320083f1c520c3a17372521c
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  - libllvm19 >=19.1.5,<19.2.0a0
+  - libllvm19 >=19.1.6,<19.2.0a0
   - libstdcxx >=13
   arch: x86_64
   platform: linux
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 11823152
-  timestamp: 1733340449539
+  size: 11819857
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda
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   md5: d4529f4dff3057982a7617c7ac58fde3
@@ -9166,9 +9169,9 @@ packages:
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   size: 385098
   timestamp: 1734000160270
-- conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-19.1.5-hf95d169_0.conda
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-  md5: a20d4ea6839510372d1eeb8532b09acf
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-19.1.6-hf95d169_1.conda
+  sha256: c40661648c34c08e21b69e0eec021ccaf090ffff070d2a9cbcb1519e1b310568
+  md5: 1bad6c181a0799298aad42fc5a7e98b7
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@@ -9176,11 +9179,11 @@ packages:
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 529401
-  timestamp: 1733291621685
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.5-ha82da77_0.conda
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-  md5: 3c7be0df28ccda1d193ea6de56dcb5ff
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+  timestamp: 1734494305140
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-19.1.6-ha82da77_1.conda
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+  md5: ce5252d8db110cdb4ae4173d0a63c7c5
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@@ -9188,11 +9191,11 @@ packages:
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 519819
-  timestamp: 1733291654212
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.22-hb9d3cd8_0.conda
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-  md5: b422943d5d772b7cc858b36ad2a92db5
+  size: 520992
+  timestamp: 1734494699681
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.23-h4ddbbb0_0.conda
+  sha256: 511d801626d02f4247a04fff957cc6e9ec4cc7e8622bd9acd076bcdc5de5fe66
+  md5: 8dfae1d2e74767e9ce36d5fa0d8605db
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@@ -9201,11 +9204,11 @@ packages:
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   license_family: MIT
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.22-h00291cd_0.conda
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-  md5: a15785ccc62ae2a8febd299424081efb
+  size: 72255
+  timestamp: 1734373823254
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.23-he65b83e_0.conda
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+  md5: 120f8f7ba6a8defb59f4253447db4bb4
   depends:
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@@ -9213,11 +9216,11 @@ packages:
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   license_family: MIT
   purls: []
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-  timestamp: 1728177128525
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.22-hd74edd7_0.conda
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-  md5: 2d3e3f3d8ab315748420ef58d5a3ae0f
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.23-hec38601_0.conda
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+  md5: 1d8b9588be14e71df38c525767a1ac30
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@@ -9225,8 +9228,8 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 54089
-  timestamp: 1728177149927
+  size: 54132
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.124-hb9d3cd8_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-25_linux64_openblas.conda
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-  md5: 4dc03a53fc69371a6158d0ed37214cd3
+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-26_linux64_openblas.conda
+  build_number: 26
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+  md5: 3792604c43695d6a273bc5faaac47d48
   depends:
-  - libblas 3.9.0 25_linux64_openblas
+  - libblas 3.9.0 26_linux64_openblas
   constrains:
-  - liblapacke 3.9.0 25_linux64_openblas
-  - libcblas 3.9.0 25_linux64_openblas
+  - libcblas 3.9.0 26_linux64_openblas
+  - liblapacke 3.9.0 26_linux64_openblas
   - blas * openblas
   arch: x86_64
   platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15608
-  timestamp: 1729642910812
+  size: 16338
+  timestamp: 1734432576650
 - conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-17_osx64_openblas.conda
   build_number: 17
   sha256: 7ce76f3e9578b62fbd88c094f343c1b09ec3466afccfc08347d31742e6831e97
@@ -9918,23 +9921,23 @@ packages:
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   timestamp: 1685931022827
-- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-25_osx64_openblas.conda
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-  md5: dda0e24b4605ebbd381e48606a107bed
+- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-26_osx64_openblas.conda
+  build_number: 26
+  sha256: 166b07a129d122dbe90b06b582b5c29fbe5b958547fb474ca497cb084846810d
+  md5: c0c54bb6382ff1e52bf08f1da539e9b4
   depends:
-  - libblas 3.9.0 25_osx64_openblas
+  - libblas 3.9.0 26_osx64_openblas
   constrains:
-  - liblapacke 3.9.0 25_osx64_openblas
+  - libcblas 3.9.0 26_osx64_openblas
   - blas * openblas
-  - libcblas 3.9.0 25_osx64_openblas
+  - liblapacke 3.9.0 26_osx64_openblas
   arch: x86_64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 15852
-  timestamp: 1729643174413
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-20_osxarm64_openblas.conda
   build_number: 20
   sha256: e13f79828a7752f6e0a74cbe62df80c551285f6c37de86bc3bd9987c97faca57
@@ -9952,43 +9955,43 @@ packages:
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