diff --git a/fastcan/_cancorr_fast.pyx b/fastcan/_cancorr_fast.pyx
index 257f4dd..ae4c30c 100644
--- a/fastcan/_cancorr_fast.pyx
+++ b/fastcan/_cancorr_fast.pyx
@@ -8,12 +8,12 @@ from cython.parallel import prange
 from scipy.linalg.cython_blas cimport isamax, idamax
 from sklearn.utils._cython_blas cimport ColMajor, NoTrans
 from sklearn.utils._cython_blas cimport _dot, _scal, _nrm2, _gemm, _axpy
-from sklearn.utils._typedefs cimport int32_t
+from sklearn.utils._typedefs cimport int32_t, uint8_t
 
 
 @final
 cdef int _bsum(
-    const bint* x,
+    const uint8_t* x,
     int n,
 ) noexcept nogil:
     """Computes the sum of the vector of bool elements.
@@ -129,6 +129,7 @@ cpdef int _forward_search(
     floating tol,                     # IN
     int num_threads,                  # IN
     int verbose,                      # IN
+    uint8_t[::1] mask,                # IN/TEMP
     int32_t[::1] indices,             # OUT
     floating[::1] scores,             # OUT
 ) except -1 nogil:
@@ -140,6 +141,7 @@ cpdef int _forward_search(
               is orthonormal to selected features and M.
     t : Non-negative integer. The number of features to be selected.
     tol : Tolerance for linear dependence check.
+    mask (n_features, ) Mask for candidate features.
     indices: (t,) The indices vector of selected features, initiated with -1.
     scores: (t,) The h-correlation/eta-cosine of selected features.
     """
@@ -149,7 +151,6 @@ cpdef int _forward_search(
         # OpenMP (in Windows) requires signed integral for prange
         int n_features = X.shape[1]
         floating* r2 = <floating*> malloc(sizeof(floating) * n_features)
-        bint* mask = <bint*> malloc(sizeof(bint) * n_features)
         floating g, ssc = 0.0
         int i, j
         int index = -1
@@ -160,7 +161,8 @@ cpdef int _forward_search(
         if i == 0:
             # Preprocessing
             for j in range(n_features):
-                mask[j] = _normv(&X[0, j], n_samples)
+                if not mask[j]:
+                    mask[j] = _normv(&X[0, j], n_samples)
         else:
             mask[index] = True
             r2[index] = 0
@@ -204,5 +206,4 @@ cpdef int _forward_search(
         with gil:
             print()
     free(r2)
-    free(mask)
     return 0
diff --git a/fastcan/_fastcan.py b/fastcan/_fastcan.py
index 4cacf80..1f3d3ee 100644
--- a/fastcan/_fastcan.py
+++ b/fastcan/_fastcan.py
@@ -29,6 +29,9 @@ class FastCan(SelectorMixin, BaseEstimator):
     indices_include : array-like of shape (n_inclusions,), default=None
         The indices of the prerequisite features.
 
+    indices_exclude : array-like of shape (n_exclusions,), default=None
+        The indices of the excluded features.
+
     eta : bool, default=False
         Whether to use eta-cosine method.
 
@@ -63,6 +66,16 @@ class FastCan(SelectorMixin, BaseEstimator):
         The h-correlation/eta-cosine of selected features. The order of
         the scores is corresponding to the feature selection process.
 
+    X_transformed_ : ndarray of shape (n_samples_, n_features), dtype=float, order='F'
+        Transformed feature matrix.
+        When h-correlation method is used, n_samples_ = n_samples.
+        When eta-cosine method is used, n_samples_ = n_features+n_outputs.
+
+    y_transformed_ : ndarray of shape (n_samples_, n_outputs), dtype=float, order='F'
+        Transformed target matrix.
+        When h-correlation method is used, n_samples_ = n_samples.
+        When eta-cosine method is used, n_samples_ = n_features+n_outputs.
+
     References
     ----------
     * Zhang, S., & Lang, Z. Q. (2022).
@@ -88,6 +101,7 @@ class FastCan(SelectorMixin, BaseEstimator):
             Interval(Integral, 1, None, closed="left"),
         ],
         "indices_include": [None, "array-like"],
+        "indices_exclude": [None, "array-like"],
         "eta": ["boolean"],
         "tol": [Interval(Real, 0, None, closed="neither")],
         "verbose": ["verbose"],
@@ -97,12 +111,14 @@ def __init__(
         self,
         n_features_to_select=1,
         indices_include=None,
+        indices_exclude=None,
         eta=False,
         tol=0.01,
         verbose=1,
     ):
         self.n_features_to_select = n_features_to_select
         self.indices_include = indices_include
+        self.indices_exclude = indices_exclude
         self.eta = eta
         self.tol = tol
         self.verbose = verbose
@@ -152,17 +168,6 @@ def fit(self, X, y):
             # [:, np.newaxis] that does not.
             y = y.reshape(-1, 1)
 
-        # indices_include
-        if self.indices_include is None:
-            indices_include = np.zeros(0, dtype=int)
-        else:
-            indices_include = check_array(
-                self.indices_include,
-                ensure_2d=False,
-                dtype=int,
-                ensure_min_samples=0,
-            )
-
         n_samples, n_features = X.shape
         n_outputs = y.shape[1]
 
@@ -172,29 +177,12 @@ def fit(self, X, y):
                 f"must be <= n_features {n_features}."
             )
 
-        if indices_include.ndim != 1:
-            raise ValueError(
-                f"Found indices_include with dim {indices_include.ndim}, "
-                "but expected == 1."
-            )
-
-        if indices_include.size >= n_features:
-            raise ValueError(
-                f"n_inclusions {indices_include.size} must "
-                f"be < n_features {n_features}."
-            )
-
-        if np.any((indices_include < 0) | (indices_include >= n_features)):
-            raise ValueError(
-                "Out of bounds. "
-                f"All items in indices_include should be in [0, {n_features}). "
-                f"But got indices_include = {indices_include}."
-            )
-
         if (n_samples < n_features + n_outputs) and self.eta:
             raise ValueError(
                 "`eta` cannot be True, when n_samples < n_features+n_outputs."
             )
+        indices_include = self._check_indices_params(self.indices_include, n_features)
+        indices_exclude = self._check_indices_params(self.indices_exclude, n_features)
 
         if self.eta:
             xy_hstack = np.hstack((X, y))
@@ -204,23 +192,28 @@ def fit(self, X, y):
             )[1:]
             qxy_transformed = singular_values.reshape(-1, 1) * unitary_arrays
             qxy_transformed = np.asfortranarray(qxy_transformed)
-            X_transformed = qxy_transformed[:, :n_features]
-            y_transformed = orth(qxy_transformed[:, n_features:])
+            self.X_transformed_ = qxy_transformed[:, :n_features]
+            self.y_transformed_ = orth(qxy_transformed[:, n_features:])
         else:
-            X_transformed = X - X.mean(0)
-            y_transformed = orth(y - y.mean(0))
+            self.X_transformed_ = X - X.mean(0)
+            self.y_transformed_ = orth(y - y.mean(0))
+
+        # initiated with -1
+        indices = np.full(self.n_features_to_select, -1, dtype=np.intc, order="F")
+        indices[: indices_include.size] = indices_include
+        scores = np.zeros(self.n_features_to_select, dtype=float, order="F")
+        mask = np.zeros(n_features, dtype=np.ubyte, order="F")
+        mask[indices_exclude] = True
 
-        indices, scores = self._prepare_data(
-            indices_include,
-        )
         n_threads = _openmp_effective_n_threads()
         _forward_search(
-            X=X_transformed,
-            V=y_transformed,
+            X=self.X_transformed_,
+            V=self.y_transformed_,
             t=self.n_features_to_select,
             tol=self.tol,
             num_threads=n_threads,
             verbose=self.verbose,
+            mask=mask,
             indices=indices,
             scores=scores,
         )
@@ -231,34 +224,37 @@ def fit(self, X, y):
         self.scores_ = scores
         return self
 
-    def _prepare_data(self, indices_include):
-        """Prepare data for _forward_search()
-        When h-correlation method is used, n_samples_ = n_samples.
-        When eta-cosine method is used, n_samples_ = n_features+n_outputs.
-
-        Parameters
-        ----------
-        indices_include : array-like of shape (n_inclusions,), dtype=int
-        The indices of the prerequisite features.
+    def _check_indices_params(self, indices_params, n_features):
+        """Check indices_include or indices_exclude."""
+        if indices_params is None:
+            indices_params = np.zeros(0, dtype=int)
+        else:
+            indices_params = check_array(
+                indices_params,
+                ensure_2d=False,
+                dtype=int,
+                ensure_min_samples=0,
+            )
 
-        Returns
-        -------
-        mask : ndarray of shape (n_features,), dtype=np.ubyte, order='F'
-            Mask for invalid candidate features.
-            The data type is unsigned char.
+        if indices_params.ndim != 1:
+            raise ValueError(
+                f"Found indices_params with dim {indices_params.ndim}, "
+                "but expected == 1."
+            )
 
-        indices: ndarray of shape (n_features_to_select,), dtype=np.intc, order='F'
-            The indices vector of selected features, initiated with -1.
-            The data type is signed int.
+        if indices_params.size >= n_features:
+            raise ValueError(
+                f"The number of indices in indices_params {indices_params.size} must "
+                f"be < n_features {n_features}."
+            )
 
-        scores: ndarray of shape (n_features_to_select,), dtype=float, order='F'
-            The h-correlation/eta-cosine of selected features.
-        """
-        # initiated with -1
-        indices = np.full(self.n_features_to_select, -1, dtype=np.intc, order="F")
-        indices[: indices_include.size] = indices_include
-        scores = np.zeros(self.n_features_to_select, dtype=float, order="F")
-        return indices, scores
+        if np.any((indices_params < 0) | (indices_params >= n_features)):
+            raise ValueError(
+                "Out of bounds. "
+                f"All items in indices_params should be in [0, {n_features}). "
+                f"But got indices_params = {indices_params}."
+            )
+        return indices_params
 
     def _get_support_mask(self):
         check_is_fitted(self)
diff --git a/pixi.lock b/pixi.lock
index 2c36304..8d06b2d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -9,90 +9,90 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/black-24.10.0-py313h78bf25f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/black-24.10.0-py312h7900ff3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.7-unix_pyh707e725_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.4-py313h8060acc_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.11-py313hc66aa0d_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.16.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.4-py312h178313f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.11-py312h8fd2918_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.16.6-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-25_linux64_openblas.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.3-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.47.0-hadc24fc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-hc0a3c3a_1.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.13.0-py313h536fd9c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/meson-python-0.17.1-pyh70fd9c4_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-6.0.0-py313h536fd9c_2.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.12.1-pyhd8ed1ab_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024b-hc8b5060_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2
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-  md5: 381bbd2a92c863f640a55b6ff3c35161
+  url: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda
+  sha256: d10e93d759931ffb6372b45d65ff34d95c6000c61a07e298d162a3bc2accebb0
+  md5: 0424ae29b104430108f5218a66db7260
   constrains:
-  - python 3.13.* *_cp313
+  - python 3.12.* *_cpython
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 6217
-  timestamp: 1723823393322
+  size: 6238
+  timestamp: 1723823388266
 - kind: conda
   name: python_abi
   version: '3.13'
@@ -4842,85 +4842,87 @@ packages:
   url: https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl
   sha256: 70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6
   requires_dist:
-  - charset-normalizer<4,>=2
-  - idna<4,>=2.5
-  - urllib3<3,>=1.21.1
+  - charset-normalizer>=2,<4
+  - idna>=2.5,<4
+  - urllib3>=1.21.1,<3
   - certifi>=2017.4.17
-  - pysocks!=1.5.7,>=1.5.6 ; extra == 'socks'
-  - chardet<6,>=3.0.2 ; extra == 'use-chardet-on-py3'
+  - pysocks>=1.5.6,!=1.5.7 ; extra == 'socks'
+  - chardet>=3.0.2,<6 ; extra == 'use-chardet-on-py3'
   requires_python: '>=3.8'
 - kind: conda
   name: ruff
-  version: 0.7.0
-  build: py313h3973d75_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/ruff-0.7.0-py313h3973d75_0.conda
-  sha256: c3cc0c59705aa1305d0139f79f729360c8ea733e329bb805e87afbb5d7fb4560
-  md5: 08a7f4fa6ad5f06477bde9286a79e548
+  version: 0.7.3
+  build: py312h2156523_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.7.3-py312h2156523_0.conda
+  sha256: 313110b1c431fe61cb3fe5e1cc4f1ffce905e95aab559055d1a86039c8ea7e35
+  md5: ed126dd09a1f2081996c5b71423a2b80
   depends:
-  - __osx >=10.13
-  - libcxx >=17
-  - python >=3.13,<3.14.0a0
-  - python_abi 3.13.* *_cp313
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
   constrains:
-  - __osx >=10.12
+  - __glibc >=2.17
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/ruff?source=hash-mapping
-  size: 6674688
-  timestamp: 1729519279743
+  size: 7767902
+  timestamp: 1731084470644
 - kind: conda
   name: ruff
-  version: 0.7.0
-  build: py313h7fc4895_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/ruff-0.7.0-py313h7fc4895_0.conda
-  sha256: f1fbcaa351eec08b50d64c5e0973ca2018f606d554343bc0d96b6fc6242225de
-  md5: 7e1d52c1895aee6eb43f8cac874a9063
+  version: 0.7.3
+  build: py313h2493e73_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/ruff-0.7.3-py313h2493e73_0.conda
+  sha256: da697673f5b0a262e23bd06c2c7da981a8030bb71fea65211461cb2a074559e2
+  md5: 7839d705328bbd8bbea6d0640883ef22
   depends:
+  - __osx >=10.13
+  - libcxx >=18
   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  constrains:
+  - __osx >=10.13
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/ruff?source=hash-mapping
-  size: 6865573
-  timestamp: 1729519869648
+  size: 7150721
+  timestamp: 1731084661752
 - kind: conda
   name: ruff
-  version: 0.7.0
-  build: py313h890c02e_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.7.0-py313h890c02e_0.conda
-  sha256: fbf2096c9a9fe35956ae214e0cba88efb9c12f578b32f437fccebb6c95de911f
-  md5: c64e3b366ae473102289e165eadd102e
+  version: 0.7.3
+  build: py313h331c231_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/ruff-0.7.3-py313h331c231_0.conda
+  sha256: 4c301d063b1bb5ef793c4e28a34836cd0cb8656680b067646f3b2be909cf30f8
+  md5: 1c3729796124b0fcb53a017ca9a348ce
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc >=13
-  - libstdcxx >=13
   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/ruff?source=hash-mapping
-  size: 6976990
-  timestamp: 1729519076999
+  size: 6778389
+  timestamp: 1731085064447
 - kind: conda
   name: ruff
-  version: 0.7.0
-  build: py313ha6603fc_0
+  version: 0.7.3
+  build: py313heab95af_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.7.0-py313ha6603fc_0.conda
-  sha256: 84134c25fbeda23180fc8bb5558932dd885084d4516767f54e42217567a2e417
-  md5: 330fab055e9c8b8525fe6de3c518277f
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.7.3-py313heab95af_0.conda
+  sha256: 45ece98aeb19cfda0e6cb6726e3a7afaccd50ccb70f6f1dbe9c8342bf6f6662c
+  md5: 56268c148f06f7d4bbd3b3940a2b873e
   depends:
   - __osx >=11.0
-  - libcxx >=17
+  - libcxx >=18
   - python >=3.13,<3.14.0a0
   - python >=3.13,<3.14.0a0 *_cp313
   - python_abi 3.13.* *_cp313
@@ -4930,8 +4932,34 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/ruff?source=hash-mapping
-  size: 6329752
-  timestamp: 1729519926110
+  size: 6858893
+  timestamp: 1731084445790
+- kind: conda
+  name: scikit-learn
+  version: 1.5.2
+  build: py312h7a48858_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.2-py312h7a48858_1.conda
+  sha256: 3118b687c7cfb4484cc5c65591b611d834e3ea2424cb75e1e0b0980d0de72afc
+  md5: 6b5f4c68483bd0c22bca9094dafc606b
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - _openmp_mutex >=4.5
+  - joblib >=1.2.0
+  - libgcc >=13
+  - libstdcxx >=13
+  - numpy >=1.19,<3
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - scipy
+  - threadpoolctl >=3.1.0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/scikit-learn?source=hash-mapping
+  size: 10393222
+  timestamp: 1726083382159
 - kind: conda
   name: scikit-learn
   version: 1.5.2
@@ -5009,31 +5037,34 @@ packages:
   size: 9331778
   timestamp: 1726083562938
 - kind: conda
-  name: scikit-learn
-  version: 1.5.2
-  build: py313h8ef605b_1
+  name: scipy
+  version: 1.14.1
+  build: py312h62794b6_1
   build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.5.2-py313h8ef605b_1.conda
-  sha256: 5692b3af46eb4b5a16150189782b9603b3afb2f63311592e09732f1efbd6e1c0
-  md5: 4c286f27b4f5b8c622d1481e3e057d1f
+  url: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.14.1-py312h62794b6_1.conda
+  sha256: d069a64edade554261672d8febf4756aeb56a6cb44bd91844eaa944e5d9f4eb9
+  md5: b43233a9e2f62fb94affe5607ea79473
   depends:
   - __glibc >=2.17,<3.0.a0
-  - _openmp_mutex >=4.5
-  - joblib >=1.2.0
+  - libblas >=3.9.0,<4.0a0
+  - libcblas >=3.9.0,<4.0a0
   - libgcc >=13
+  - libgfortran
+  - libgfortran5 >=13.3.0
+  - liblapack >=3.9.0,<4.0a0
   - libstdcxx >=13
-  - numpy >=1.21,<3
-  - python >=3.13.0rc2,<3.14.0a0
-  - python_abi 3.13.* *_cp313
-  - scipy
-  - threadpoolctl >=3.1.0
+  - numpy <2.3
+  - numpy >=1.19,<3
+  - numpy >=1.23.5
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/scikit-learn?source=hash-mapping
-  size: 10497980
-  timestamp: 1726083319119
+  - pkg:pypi/scipy?source=hash-mapping
+  size: 17622722
+  timestamp: 1729481826601
 - kind: conda
   name: scipy
   version: 1.14.1
@@ -5061,35 +5092,6 @@ packages:
   - pkg:pypi/scipy?source=hash-mapping
   size: 16018018
   timestamp: 1729482196516
-- kind: conda
-  name: scipy
-  version: 1.14.1
-  build: py313h27c5614_1
-  build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.14.1-py313h27c5614_1.conda
-  sha256: e42a0702ae5c2d45e4ee85d50b8dfbfe4966cab51e8216c9ae7bd40c6c3c4189
-  md5: c5c52b95724a6d4adb72499912eea085
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - libblas >=3.9.0,<4.0a0
-  - libcblas >=3.9.0,<4.0a0
-  - libgcc >=13
-  - libgfortran
-  - libgfortran5 >=13.3.0
-  - liblapack >=3.9.0,<4.0a0
-  - libstdcxx >=13
-  - numpy <2.4
-  - numpy >=1.21,<3
-  - numpy >=1.23.5
-  - python >=3.13,<3.14.0a0
-  - python_abi 3.13.* *_cp313
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/scipy?source=hash-mapping
-  size: 17682454
-  timestamp: 1729482237410
 - kind: conda
   name: scipy
   version: 1.14.1
@@ -5149,21 +5151,21 @@ packages:
   timestamp: 1729481595130
 - kind: conda
   name: setuptools
-  version: 75.1.0
+  version: 75.3.0
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.1.0-pyhd8ed1ab_0.conda
-  sha256: 6725235722095c547edd24275053c615158d6163f396550840aebd6e209e4738
-  md5: d5cd48392c67fb6849ba459c2c2b671f
+  url: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.3.0-pyhd8ed1ab_0.conda
+  sha256: a36d020b9f32fc3f1a6488a1c4a9c13988c6468faf6895bf30ca69521a61230e
+  md5: 2ce9825396daf72baabaade36cee16da
   depends:
   - python >=3.8
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/setuptools?source=hash-mapping
-  size: 777462
-  timestamp: 1727249510532
+  size: 779561
+  timestamp: 1730382173961
 - kind: conda
   name: sigtool
   version: 0.1.3
@@ -5568,21 +5570,21 @@ packages:
   timestamp: 1604308660817
 - kind: conda
   name: tomli
-  version: 2.0.2
-  build: pyhd8ed1ab_0
+  version: 2.1.0
+  build: pyhff2d567_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.2-pyhd8ed1ab_0.conda
-  sha256: 5e742ba856168b606ac3c814d247657b1c33b8042371f1a08000bdc5075bc0cc
-  md5: e977934e00b355ff55ed154904044727
+  url: https://conda.anaconda.org/conda-forge/noarch/tomli-2.1.0-pyhff2d567_0.conda
+  sha256: 354b8a64d4f3311179d85aefc529ca201a36afc1af090d0010c46be7b79f9a47
+  md5: 3fa1089b4722df3a900135925f4519d9
   depends:
-  - python >=3.7
+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/tomli?source=hash-mapping
-  size: 18203
-  timestamp: 1727974767524
+  size: 18741
+  timestamp: 1731426862834
 - kind: conda
   name: typing_extensions
   version: 4.12.2
@@ -5636,18 +5638,18 @@ packages:
   requires_dist:
   - brotli>=1.0.9 ; platform_python_implementation == 'CPython' and extra == 'brotli'
   - brotlicffi>=0.8.0 ; platform_python_implementation != 'CPython' and extra == 'brotli'
-  - h2<5,>=4 ; extra == 'h2'
-  - pysocks!=1.5.7,<2.0,>=1.5.6 ; extra == 'socks'
+  - h2>=4,<5 ; extra == 'h2'
+  - pysocks>=1.5.6,!=1.5.7,<2.0 ; extra == 'socks'
   - zstandard>=0.18.0 ; extra == 'zstd'
   requires_python: '>=3.8'
 - kind: conda
   name: uv
-  version: 0.4.25
+  version: 0.5.1
   build: h0f3a69f_0
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/uv-0.4.25-h0f3a69f_0.conda
-  sha256: 66ec8fc80cebbabef8a43a8a420f6540399200dcc0c9195db0bdda2836eee7be
-  md5: f820dec118406e896be62db8c53151ad
+  url: https://conda.anaconda.org/conda-forge/linux-64/uv-0.5.1-h0f3a69f_0.conda
+  sha256: 9bda0a65af5d2865cfe887dd90dbf04ed97bf6920a1c223396036e1c1f831f58
+  md5: 2f0601933384868b7c34193247a1b167
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -5656,58 +5658,58 @@ packages:
   - __glibc >=2.17
   license: Apache-2.0 OR MIT
   purls: []
-  size: 9403871
-  timestamp: 1729526779016
+  size: 9804886
+  timestamp: 1731134694659
 - kind: conda
   name: uv
-  version: 0.4.25
-  build: h3a35632_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/uv-0.4.25-h3a35632_0.conda
-  sha256: db4dd667b1e78199412010a125c796c5bc9802ecc2d0e1c10f706cbb42c69417
-  md5: c27beb3bc2723f2d3065e4d6a94657e7
+  version: 0.5.1
+  build: h668ec48_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/uv-0.5.1-h668ec48_0.conda
+  sha256: a9275898733ebbe40ad0b3a3f2ca7b7c8861309d48396e188f6ba433a7206de1
+  md5: 96791a25fa69cd8db922861f257adccd
   depends:
-  - __osx >=10.13
-  - libcxx >=17
+  - __osx >=11.0
+  - libcxx >=18
   constrains:
-  - __osx >=10.13
+  - __osx >=11.0
   license: Apache-2.0 OR MIT
   purls: []
-  size: 9047274
-  timestamp: 1729527682448
+  size: 8674748
+  timestamp: 1731135650399
 - kind: conda
   name: uv
-  version: 0.4.25
-  build: h41fe3af_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/uv-0.4.25-h41fe3af_0.conda
-  sha256: 365063aa057bdb0f755449bb1f9490462c191109071a4ebb6b9648587558109e
-  md5: f736f9177b89edfccfe67650cbd05839
+  version: 0.5.1
+  build: h8de1528_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/uv-0.5.1-h8de1528_0.conda
+  sha256: ef5af89267bfadec1fc8b1b7183a8f1af686147e6f6fc5b19b87c56fa9136228
+  md5: b09d0da73064bde62006e802df19f44e
   depends:
-  - __osx >=11.0
-  - libcxx >=17
+  - __osx >=10.13
+  - libcxx >=18
   constrains:
-  - __osx >=11.0
+  - __osx >=10.13
   license: Apache-2.0 OR MIT
   purls: []
-  size: 8475727
-  timestamp: 1729527780770
+  size: 9344837
+  timestamp: 1731135239423
 - kind: conda
   name: uv
-  version: 0.4.25
+  version: 0.5.1
   build: ha08ef0e_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/uv-0.4.25-ha08ef0e_0.conda
-  sha256: 74ae665e93b8ed325ea30864c2d341ee57c1c4878dba3bf2781828b1be58031d
-  md5: 2162d0190d22bbf556365c076f60739c
+  url: https://conda.anaconda.org/conda-forge/win-64/uv-0.5.1-ha08ef0e_0.conda
+  sha256: 14c1a23e7ef6694d1bd7eef5edb9a5819d19ddf1006a5deda012d017932292f3
+  md5: 442819ddf83c287ddd3078477cafd57d
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   license: Apache-2.0 OR MIT
   purls: []
-  size: 10356663
-  timestamp: 1729527565706
+  size: 10691540
+  timestamp: 1731135656601
 - kind: conda
   name: vc
   version: '14.3'
@@ -5815,21 +5817,21 @@ packages:
   timestamp: 1660346976440
 - kind: conda
   name: zipp
-  version: 3.20.2
+  version: 3.21.0
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda
-  sha256: 1e84fcfa41e0afdd87ff41e6fbb719c96a0e098c1f79be342293ab0bd8dea322
-  md5: 4daaed111c05672ae669f7036ee5bba3
+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda
+  sha256: 232a30e4b0045c9de5e168dda0328dc0e28df9439cdecdfb97dd79c1c82c4cec
+  md5: fee389bf8a4843bd7a2248ce11b7f188
   depends:
   - python >=3.8
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/zipp?source=hash-mapping
-  size: 21409
-  timestamp: 1726248679175
+  size: 21702
+  timestamp: 1731262194278
 - kind: conda
   name: zlib
   version: 1.3.1
diff --git a/tests/test_fastcan.py b/tests/test_fastcan.py
index 0be407d..610fb73 100644
--- a/tests/test_fastcan.py
+++ b/tests/test_fastcan.py
@@ -58,13 +58,14 @@ def test_select_kbest_classif():
     gtruth[:n_informative] = 1
     assert_array_equal(support, gtruth)
 
-def test_indices_include():
+def test_indices_include_exclude():
     # Test whether fastcan can select informative features based
-    # on some pre-include features
+    # on some pre-include features and pre-exclude features
     n_samples = 20
     n_features = 20
     n_targets = 8
     n_informative = 5
+    indices_params = [0, 3]
 
     X, y = make_regression(
         n_samples=n_samples,
@@ -76,16 +77,24 @@ def test_indices_include():
         random_state=0,
     )
 
-    correlation_filter = FastCan(
+    include_filter = FastCan(
         n_features_to_select=n_informative,
-        indices_include=[0, 3]
+        indices_include=indices_params
     )
-    correlation_filter.fit(X, y)
+    exclude_filter = FastCan(
+        n_features_to_select=n_informative,
+        indices_exclude=indices_params
+    )
+    include_filter.fit(X, y)
+    exclude_filter.fit(X, y)
 
-    support = correlation_filter.get_support()
+    include_support = include_filter.get_support()
+    exclude_support = exclude_filter.get_support()
     gtruth = np.zeros(n_features)
     gtruth[:n_informative] = 1
-    assert_array_equal(support, gtruth)
+    assert_array_equal(include_support, gtruth)
+    gtruth[indices_params] = 0
+    assert_array_equal(exclude_support[:n_informative], gtruth[:n_informative])
 
 def test_ssc_consistent_with_cca():
     # Test whether the ssc got from the fastcan is consistent
@@ -193,13 +202,13 @@ def test_raise_errors():
     with pytest.raises(ValueError, match=r"n_features_to_select .*"):
         selector_n_select.fit(X, y)
 
-    with pytest.raises(ValueError, match=r"n_inclusions .*"):
+    with pytest.raises(ValueError, match=r"The number of indices .*"):
         selector_n_inclusions.fit(X, y)
 
     with pytest.raises(ValueError, match=r"Out of bounds. .*"):
         selector_indices_include_bounds.fit(X, y)
 
-    with pytest.raises(ValueError, match=r"Found indices_include with dim .*"):
+    with pytest.raises(ValueError, match=r"Found indices_params with dim .*"):
         selector_indices_include_ndim.fit(X, y)
 
     with pytest.raises(ValueError, match=r"`eta` cannot be True, .*"):
@@ -223,3 +232,5 @@ def test_cython_errors():
     with pytest.raises(RuntimeError, match=r"No candidate feature can .*"):
         # No candidate
         selector_no_cand.fit(np.c_[x_sub, x_sub[:, 0]+x_sub[:, 1]], y)
+
+test_indices_include_exclude()