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diff --git a/docs/_downloads/06bb0f682e15138dfd659b791a14e9c6/plot_variable_importance_classif.zip b/docs/_downloads/06bb0f682e15138dfd659b791a14e9c6/plot_variable_importance_classif.zip
diff --git a/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/docs/_downloads/117422582fb46cc8ed6549598a2d87de/plot_dcrt_example.zip b/docs/_downloads/117422582fb46cc8ed6549598a2d87de/plot_dcrt_example.zip
diff --git a/docs/_downloads/1a99dc8cb1c22f91072d67cf26fce26c/plot_knockoff_aggregation.zip b/docs/_downloads/1a99dc8cb1c22f91072d67cf26fce26c/plot_knockoff_aggregation.zip
diff --git a/docs/_downloads/5ca231767268e6cd969e65225d673650/plot_fmri_data_example.zip b/docs/_downloads/5ca231767268e6cd969e65225d673650/plot_fmri_data_example.zip
diff --git a/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/..._downloads/707d94040f5ada342e781499193f46f1/plot_diabetes_variable_importance_example.zip b/..._downloads/707d94040f5ada342e781499193f46f1/plot_diabetes_variable_importance_example.zip
diff --git a/docs/_downloads/c983a5ad5887eff1a3df617cbd64234a/plot_variable_importance_classif.ipynb b/docs/_downloads/c983a5ad5887eff1a3df617cbd64234a/plot_variable_importance_classif.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.lines as mlines\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.linalg import toeplitz\nfrom scipy.stats import ttest_1samp\nfrom sklearn.base import clone\nfrom sklearn.linear_model import RidgeCV\nfrom sklearn.metrics import log_loss\nfrom sklearn.model_selection import RandomizedSearchCV, StratifiedKFold\nfrom sklearn.svm import SVC\n\nfrom hidimstat import CPI, PermutationImportance"
+        "import matplotlib.lines as mlines\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.linalg import toeplitz\nfrom scipy.stats import ttest_1samp\nfrom sklearn.base import clone\nfrom sklearn.linear_model import RidgeCV\nfrom sklearn.metrics import hinge_loss\nfrom sklearn.model_selection import RandomizedSearchCV, StratifiedKFold\nfrom sklearn.svm import SVC\n\nfrom hidimstat import CPI, PermutationImportance"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "seed = 0\ncv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\nimportance_linear = []\nimportance_non_linear = []\nimportant_pi = []\n\nmodel_linear = RandomizedSearchCV(\n    SVC(random_state=seed, kernel=\"linear\"),\n    param_distributions={\n        \"C\": np.logspace(-3, 3, 10),\n    },\n    n_iter=10,\n    n_jobs=5,\n    random_state=seed,\n)\nmodel_non_linear = RandomizedSearchCV(\n    SVC(\n        random_state=seed,\n        kernel=\"poly\",\n        degree=2,\n        coef0=1,\n    ),\n    param_distributions={\n        \"C\": np.logspace(-3, 3, 10),\n    },\n    n_iter=10,\n    n_jobs=5,\n    random_state=seed,\n)\nimputation_model = RidgeCV(alphas=np.logspace(-3, 3, 50))\n\n\nimportance_list = []\nfor train, test in cv.split(X, y):\n    model_linear_c = clone(model_linear)\n    model_linear_c.fit(X[train], y[train])\n    cpi_linear = CPI(\n        estimator=model_linear_c,\n        imputation_model=clone(imputation_model),\n        n_permutations=50,\n        n_jobs=5,\n        loss=log_loss,\n        random_state=seed,\n        method=\"decision_function\",\n    )\n    cpi_linear.fit(X[train], y[train])\n    imp_cpi_linear = cpi_linear.score(X[test], y[test])[\"importance\"]\n\n    model_non_linear_c = clone(model_non_linear)\n    model_non_linear_c.fit(X[train], y[train])\n    cpi_non_linear = CPI(\n        estimator=model_non_linear_c,\n        imputation_model=clone(imputation_model),\n        n_permutations=50,\n        n_jobs=5,\n        loss=log_loss,\n        random_state=seed,\n        method=\"decision_function\",\n    )\n    cpi_non_linear.fit(X[train], y[train])\n    imp_cpi_non_linear = cpi_non_linear.score(X[test], y[test])[\"importance\"]\n\n    pi_non_linear = PermutationImportance(\n        estimator=model_non_linear_c,\n        n_permutations=50,\n        n_jobs=5,\n        random_state=seed,\n        method=\"decision_function\",\n    )\n    pi_non_linear.fit(X[train], y[train])\n    imp_pi_non_linear = pi_non_linear.score(X[test], y[test])[\"importance\"]\n\n    importance_list.append(\n        np.stack(\n            [\n                imp_cpi_linear,\n                imp_cpi_non_linear,\n                imp_pi_non_linear,\n            ]\n        )\n    )\n\nimportance_arr = np.stack(importance_list)"
+        "seed = 0\ncv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\nimportance_linear = []\nimportance_non_linear = []\nimportant_pi = []\n\nmodel_linear = RandomizedSearchCV(\n    SVC(random_state=seed, kernel=\"linear\"),\n    param_distributions={\n        \"C\": np.logspace(-3, 3, 10),\n    },\n    n_iter=10,\n    n_jobs=5,\n    random_state=seed,\n)\nmodel_non_linear = RandomizedSearchCV(\n    SVC(\n        random_state=seed,\n        kernel=\"poly\",\n        degree=2,\n        coef0=1,\n    ),\n    param_distributions={\n        \"C\": np.logspace(-3, 3, 10),\n    },\n    n_iter=10,\n    n_jobs=5,\n    random_state=seed,\n)\nimputation_model = RidgeCV(alphas=np.logspace(-3, 3, 50))\n\n\nimportance_list = []\nfor train, test in cv.split(X, y):\n    model_linear_c = clone(model_linear)\n    model_linear_c.fit(X[train], y[train])\n    cpi_linear = CPI(\n        estimator=model_linear_c,\n        imputation_model=clone(imputation_model),\n        n_permutations=50,\n        n_jobs=5,\n        loss=hinge_loss,\n        random_state=seed,\n        method=\"decision_function\",\n    )\n    cpi_linear.fit(X[train], y[train])\n    imp_cpi_linear = cpi_linear.score(X[test], y[test])[\"importance\"]\n\n    model_non_linear_c = clone(model_non_linear)\n    model_non_linear_c.fit(X[train], y[train])\n    cpi_non_linear = CPI(\n        estimator=model_non_linear_c,\n        imputation_model=clone(imputation_model),\n        n_permutations=50,\n        n_jobs=5,\n        loss=hinge_loss,\n        random_state=seed,\n        method=\"decision_function\",\n    )\n    cpi_non_linear.fit(X[train], y[train])\n    imp_cpi_non_linear = cpi_non_linear.score(X[test], y[test])[\"importance\"]\n\n    pi_non_linear = PermutationImportance(\n        estimator=model_non_linear_c,\n        n_permutations=50,\n        n_jobs=5,\n        random_state=seed,\n        method=\"decision_function\",\n    )\n    pi_non_linear.fit(X[train], y[train])\n    imp_pi_non_linear = pi_non_linear.score(X[test], y[test])[\"importance\"]\n\n    importance_list.append(\n        np.stack(\n            [\n                imp_cpi_linear,\n                imp_cpi_non_linear,\n                imp_pi_non_linear,\n            ]\n        )\n    )\n\nimportance_arr = np.stack(importance_list)"
       ]
     },
     {

diff --git a/docs/_downloads/e08c0f6d4aade0f0eaf8ba56dbbfd9c9/plot_2D_simulation_example.zip b/docs/_downloads/e08c0f6d4aade0f0eaf8ba56dbbfd9c9/plot_2D_simulation_example.zip
diff --git a/docs/_downloads/f5fec7ca6bed47c781d2f2dee3171c85/plot_variable_importance_classif.py b/docs/_downloads/f5fec7ca6bed47c781d2f2dee3171c85/plot_variable_importance_classif.py
@@ -29,7 +29,7 @@
 from scipy.stats import ttest_1samp
 from sklearn.base import clone
 from sklearn.linear_model import RidgeCV
-from sklearn.metrics import log_loss
+from sklearn.metrics import hinge_loss
 from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold
 from sklearn.svm import SVC
 
@@ -163,7 +163,7 @@
         imputation_model=clone(imputation_model),
         n_permutations=50,
         n_jobs=5,
-        loss=log_loss,
+        loss=hinge_loss,
         random_state=seed,
         method="decision_function",
     )
@@ -177,7 +177,7 @@
         imputation_model=clone(imputation_model),
         n_permutations=50,
         n_jobs=5,
-        loss=log_loss,
+        loss=hinge_loss,
         random_state=seed,
         method="decision_function",
     )

diff --git a/docs/_images/sphx_glr_plot_variable_importance_classif_002.png b/docs/_images/sphx_glr_plot_variable_importance_classif_002.png
diff --git a/docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt b/docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt
@@ -554,9 +554,9 @@ randomization.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 11.171 seconds)
+   **Total running time of the script:** (1 minutes 5.243 seconds)
 
-**Estimated memory usage:**  704 MB
+**Estimated memory usage:**  701 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_2D_simulation_example.py:

diff --git a/docs/_sources/auto_examples/plot_dcrt_example.rst.txt b/docs/_sources/auto_examples/plot_dcrt_example.rst.txt
@@ -162,9 +162,9 @@ Plotting the comparison
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 5.337 seconds)
+   **Total running time of the script:** (1 minutes 2.810 seconds)
 
-**Estimated memory usage:**  640 MB
+**Estimated memory usage:**  638 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_dcrt_example.py:

diff --git a/docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt b/docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt
@@ -491,9 +491,9 @@ Analyze the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.424 seconds)
+   **Total running time of the script:** (0 minutes 8.765 seconds)
 
-**Estimated memory usage:**  625 MB
+**Estimated memory usage:**  623 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_diabetes_variable_importance_example.py:

diff --git a/docs/_sources/auto_examples/plot_fmri_data_example.rst.txt b/docs/_sources/auto_examples/plot_fmri_data_example.rst.txt
@@ -176,19 +176,15 @@ You may choose a subject in [1, 2, 3, 4, 5, 6]. By default subject=2.
     [_add_readme_to_default_data_locations] Added README.md to /home/runner/nilearn_data
     [get_dataset_dir] Dataset created in /home/runner/nilearn_data/haxby2001
     [fetch_single_file] Downloading data from https://www.nitrc.org/frs/download.php/7868/mask.nii.gz ...
-    [fetch_single_file]  ...done. (1 seconds, 0 min)
+    [fetch_single_file]  ...done. (0 seconds, 0 min)
 
     [fetch_single_file] Downloading data from http://data.pymvpa.org/datasets/haxby2001/MD5SUMS ...
     [fetch_single_file]  ...done. (0 seconds, 0 min)
 
     [fetch_single_file] Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj2-2010.01.14.tar.gz ...
-    [_chunk_report_]     Downloaded 21749760 of 291168628 bytes (7.5%%,   12.7s remaining)
-    [_chunk_report_]     Downloaded 67272704 of 291168628 bytes (23.1%%,    6.8s remaining)
-    [_chunk_report_]     Downloaded 112746496 of 291168628 bytes (38.7%%,    4.8s remaining)
-    [_chunk_report_]     Downloaded 156139520 of 291168628 bytes (53.6%%,    3.5s remaining)
-    [_chunk_report_]     Downloaded 200409088 of 291168628 bytes (68.8%%,    2.3s remaining)
-    [_chunk_report_]     Downloaded 245473280 of 291168628 bytes (84.3%%,    1.1s remaining)
-    [fetch_single_file]  ...done. (7 seconds, 0 min)
+    [_chunk_report_]     Downloaded 84115456 of 291168628 bytes (28.9%%,    2.5s remaining)
+    [_chunk_report_]     Downloaded 187072512 of 291168628 bytes (64.2%%,    1.1s remaining)
+    [fetch_single_file]  ...done. (3 seconds, 0 min)
 
     [uncompress_file] Extracting data from /home/runner/nilearn_data/haxby2001/9cabe068089e791ef0c5fe930fc20e30/subj2-2010.01.14.tar.gz...
     [uncompress_file] .. done.
@@ -293,7 +289,7 @@ Now, we compute p-values thanks to permutation tests applied to
  .. code-block:: none
 
     [Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    1.6s
-    [Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.7s
+    [Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.6s
 
 
 
@@ -374,7 +370,7 @@ However you might benefit from clustering randomization taking
  .. code-block:: none
 
     [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
-    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   36.0s finished
+    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   34.2s finished
 
 
 
@@ -619,9 +615,9 @@ spurious discoveries.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 35.950 seconds)
+   **Total running time of the script:** (1 minutes 27.619 seconds)
 
-**Estimated memory usage:**  3251 MB
+**Estimated memory usage:**  3365 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_fmri_data_example.py:

diff --git a/docs/_sources/auto_examples/plot_knockoff_aggregation.rst.txt b/docs/_sources/auto_examples/plot_knockoff_aggregation.rst.txt
@@ -205,9 +205,9 @@ Imports needed for this script
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (5 minutes 51.290 seconds)
+   **Total running time of the script:** (5 minutes 39.528 seconds)
 
-**Estimated memory usage:**  785 MB
+**Estimated memory usage:**  815 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_knockoff_aggregation.py:

diff --git a/docs/_sources/auto_examples/plot_variable_importance_classif.rst.txt b/docs/_sources/auto_examples/plot_variable_importance_classif.rst.txt
@@ -52,7 +52,7 @@ Imports needed
     from scipy.stats import ttest_1samp
     from sklearn.base import clone
     from sklearn.linear_model import RidgeCV
-    from sklearn.metrics import log_loss
+    from sklearn.metrics import hinge_loss
     from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold
     from sklearn.svm import SVC
 
@@ -176,7 +176,7 @@ Visualize the data
  .. code-block:: none
 
 
-    [<matplotlib.axis.YTick object at 0x7fee88776120>, <matplotlib.axis.YTick object at 0x7fee887745c0>, <matplotlib.axis.YTick object at 0x7fee884de6f0>, <matplotlib.axis.YTick object at 0x7fee884dcc80>, <matplotlib.axis.YTick object at 0x7fee86056ff0>, <matplotlib.axis.YTick object at 0x7fee884dc680>, <matplotlib.axis.YTick object at 0x7fee884dd280>, <matplotlib.axis.YTick object at 0x7fee884dc4a0>, <matplotlib.axis.YTick object at 0x7fee8600fa70>, <matplotlib.axis.YTick object at 0x7fee884df770>]
+    [<matplotlib.axis.YTick object at 0x7fa1fa109160>, <matplotlib.axis.YTick object at 0x7fa1fa1a36b0>, <matplotlib.axis.YTick object at 0x7fa1fb042330>, <matplotlib.axis.YTick object at 0x7fa1fb0406b0>, <matplotlib.axis.YTick object at 0x7fa1fb101820>, <matplotlib.axis.YTick object at 0x7fa1fb043e30>, <matplotlib.axis.YTick object at 0x7fa1fb0414c0>, <matplotlib.axis.YTick object at 0x7fa1f94ed1c0>, <matplotlib.axis.YTick object at 0x7fa1f94ef4a0>, <matplotlib.axis.YTick object at 0x7fa1fb0410d0>]
 
 
 
@@ -236,7 +236,7 @@ estimate the importance of the features.
             imputation_model=clone(imputation_model),
             n_permutations=50,
             n_jobs=5,
-            loss=log_loss,
+            loss=hinge_loss,
             random_state=seed,
             method="decision_function",
         )
@@ -250,7 +250,7 @@ estimate the importance of the features.
             imputation_model=clone(imputation_model),
             n_permutations=50,
             n_jobs=5,
-            loss=log_loss,
+            loss=hinge_loss,
             random_state=seed,
             method="decision_function",
         )
@@ -422,16 +422,16 @@ the features.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fee8600ff20>
+    <matplotlib.legend.Legend object at 0x7fa1f94f54c0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 44.780 seconds)
+   **Total running time of the script:** (0 minutes 44.777 seconds)
 
-**Estimated memory usage:**  621 MB
+**Estimated memory usage:**  619 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_variable_importance_classif.py:

diff --git a/docs/_sources/auto_examples/sg_execution_times.rst.txt b/docs/_sources/auto_examples/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**10:37.954** total execution time for 6 files **from auto_examples**:
+**10:08.743** total execution time for 6 files **from auto_examples**:
 
 .. container::
 
@@ -33,20 +33,20 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_knockoff_aggregation.py` (``plot_knockoff_aggregation.py``)
-     - 05:51.290
-     - 785.0
+     - 05:39.528
+     - 814.6
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``plot_fmri_data_example.py``)
-     - 01:35.950
-     - 3250.8
+     - 01:27.619
+     - 3365.0
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``plot_2D_simulation_example.py``)
-     - 01:11.171
-     - 704.2
+     - 01:05.243
+     - 701.4
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``plot_dcrt_example.py``)
-     - 01:05.337
-     - 639.9
+     - 01:02.810
+     - 637.9
    * - :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py` (``plot_variable_importance_classif.py``)
-     - 00:44.780
-     - 621.0
+     - 00:44.777
+     - 618.6
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``plot_diabetes_variable_importance_example.py``)
-     - 00:09.424
-     - 625.3
+     - 00:08.765
+     - 622.7
diff --git a/docs/_sources/sg_execution_times.rst.txt b/docs/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**10:37.954** total execution time for 6 files **from all galleries**:
+**10:08.743** total execution time for 6 files **from all galleries**:
 
 .. container::
 
@@ -33,20 +33,20 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_knockoff_aggregation.py` (``../examples/plot_knockoff_aggregation.py``)
-     - 05:51.290
-     - 785.0
+     - 05:39.528
+     - 814.6
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``../examples/plot_fmri_data_example.py``)
-     - 01:35.950
-     - 3250.8
+     - 01:27.619
+     - 3365.0
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``../examples/plot_2D_simulation_example.py``)
-     - 01:11.171
-     - 704.2
+     - 01:05.243
+     - 701.4
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``../examples/plot_dcrt_example.py``)
-     - 01:05.337
-     - 639.9
+     - 01:02.810
+     - 637.9
    * - :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py` (``../examples/plot_variable_importance_classif.py``)
-     - 00:44.780
-     - 621.0
+     - 00:44.777
+     - 618.6
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``../examples/plot_diabetes_variable_importance_example.py``)
-     - 00:09.424
-     - 625.3
+     - 00:08.765
+     - 622.7
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '0.1.dev1+g85746e2',
+    VERSION: '0.1.dev1+g19e6cf7',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',

diff --git a/docs/api.html b/docs/api.html
@@ -5,15 +5,15 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
-    <title>API Documentation &#8212; HiDimStat 0.1.dev1+g85746e2 documentation</title>
+    <title>API Documentation &#8212; HiDimStat 0.1.dev1+g19e6cf7 documentation</title>
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
     <link rel="stylesheet" type="text/css" href="_static/bootstrap-sphinx.css?v=284a2d1d" />
     <link rel="stylesheet" type="text/css" href="_static/style.css?v=fbda0898" />
     <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
     <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
-    <script src="_static/documentation_options.js?v=2751c6d6"></script>
+    <script src="_static/documentation_options.js?v=4f73dee8"></script>
     <script src="_static/doctools.js?v=9bcbadda"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <link rel="index" title="Index" href="genindex.html" />

diff --git a/docs/auto_examples/index.html b/docs/auto_examples/index.html
@@ -5,15 +5,15 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
-    <title>Examples Gallery &#8212; HiDimStat 0.1.dev1+g85746e2 documentation</title>
+    <title>Examples Gallery &#8212; HiDimStat 0.1.dev1+g19e6cf7 documentation</title>
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
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