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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/76c0979bf6618aa210fd11bb28dcf896/plot_fmri_data_example.py b/docs/_downloads/76c0979bf6618aa210fd11bb28dcf896/plot_fmri_data_example.py
@@ -55,7 +55,7 @@
 from sklearn.svm import LinearSVR
 from sklearn.utils import Bunch
 
-from hidimstat.ada_svr import ada_svr
+from hidimstat.adaptative_permutation_threshold_SVR import ada_svr
 from hidimstat.clustered_inference import clustered_inference
 from hidimstat.ensemble_clustered_inference import ensemble_clustered_inference
 from hidimstat.permutation_test import permutation_test, permutation_test_pval

diff --git a/docs/_downloads/931385a6992917f918857d6a3ee9f780/plot_fmri_data_example.ipynb b/docs/_downloads/931385a6992917f918857d6a3ee9f780/plot_fmri_data_example.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nimport pandas as pd\nfrom nilearn import datasets\nfrom nilearn.image import mean_img\nfrom nilearn.input_data import NiftiMasker\nfrom nilearn.plotting import plot_stat_map, show\nfrom sklearn.cluster import FeatureAgglomeration\nfrom sklearn.feature_extraction import image\nfrom sklearn.linear_model import Ridge\nfrom sklearn.svm import LinearSVR\nfrom sklearn.utils import Bunch\n\nfrom hidimstat.ada_svr import ada_svr\nfrom hidimstat.clustered_inference import clustered_inference\nfrom hidimstat.ensemble_clustered_inference import ensemble_clustered_inference\nfrom hidimstat.permutation_test import permutation_test, permutation_test_pval\nfrom hidimstat.standardized_svr import standardized_svr\nfrom hidimstat.stat_tools import pval_from_scale, zscore_from_pval\n\nn_job = None"
+        "import numpy as np\nimport pandas as pd\nfrom nilearn import datasets\nfrom nilearn.image import mean_img\nfrom nilearn.input_data import NiftiMasker\nfrom nilearn.plotting import plot_stat_map, show\nfrom sklearn.cluster import FeatureAgglomeration\nfrom sklearn.feature_extraction import image\nfrom sklearn.linear_model import Ridge\nfrom sklearn.svm import LinearSVR\nfrom sklearn.utils import Bunch\n\nfrom hidimstat.adaptative_permutation_threshold_SVR import ada_svr\nfrom hidimstat.clustered_inference import clustered_inference\nfrom hidimstat.ensemble_clustered_inference import ensemble_clustered_inference\nfrom hidimstat.permutation_test import permutation_test, permutation_test_pval\nfrom hidimstat.standardized_svr import standardized_svr\nfrom hidimstat.stat_tools import pval_from_scale, zscore_from_pval\n\nn_job = None"
       ]
     },
     {

diff --git a/docs/_downloads/e08c0f6d4aade0f0eaf8ba56dbbfd9c9/plot_2D_simulation_example.zip b/docs/_downloads/e08c0f6d4aade0f0eaf8ba56dbbfd9c9/plot_2D_simulation_example.zip
diff --git a/docs/_sources/api.rst.txt b/docs/_sources/api.rst.txt
@@ -15,7 +15,6 @@ Functions
 .. autosummary::
    :toctree: generated/
 
-   ada_svr
    aggregate_quantiles
    clustered_inference
    data_simulation

diff --git a/docs/_sources/auto_examples/index.rst.txt b/docs/_sources/auto_examples/index.rst.txt
@@ -87,35 +87,35 @@ Examples Gallery
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example compares several methods that estimate a decoder map support with statistical guarantees. More precisely, we aim at thresholding the weights of some estimated decoder maps according to the confidence we have that they are nonzero. Here, we work with the Haxby dataset and we focus on the &#x27;face vs house&#x27; contrast. Thus, we consider the labeled activation maps of a given subject and try produce a brain map that corresponds to the discriminative pattern that makes the decoding of the two conditions.">
+    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we illustrate how to measure variable importance in a classification  context. The problem under consideration is a binary classification where the target  variable is generated using a non-linear function of the features. Therefore  illustrating the importance of model-agnostic variable importance methods, which, as  opposed to linear models for instance, can capture non-linear relationships.  The features are generated from a multivariate normal distribution with a Toeplitz  correlation matrix. This second specificity of the problem is interesting to exemplify  the benefits of the conditional permutation importance (CPI) method [:footciteChamma_NeurIPS2023]  over the standard permutation importance (PI) method [:footcitebreimanRandomForests2001].">
 
 .. only:: html
 
-  .. image:: /auto_examples/images/thumb/sphx_glr_plot_fmri_data_example_thumb.png
+  .. image:: /auto_examples/images/thumb/sphx_glr_plot_variable_importance_classif_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py`
+  :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Support recovery on fMRI data</div>
+      <div class="sphx-glr-thumbnail-title">Measuring variable importance in classification</div>
     </div>
 
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we illustrate how to measure variable importance in a classification  context. The problem under consideration is a binary classification where the target  variable is generated using a non-linear function of the features. Therefore  illustrating the importance of model-agnostic variable importance methods, which, as  opposed to linear models for instance, can capture non-linear relationships.  The features are generated from a multivariate normal distribution with a Toeplitz  correlation matrix. This second specificity of the problem is interesting to exemplify  the benefits of the conditional permutation importance (CPI) method [:footciteChamma_NeurIPS2023]  over the standard permutation importance (PI) method [:footcitebreimanRandomForests2001].">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example compares several methods that estimate a decoder map support with statistical guarantees. More precisely, we aim at thresholding the weights of some estimated decoder maps according to the confidence we have that they are nonzero. Here, we work with the Haxby dataset and we focus on the &#x27;face vs house&#x27; contrast. Thus, we consider the labeled activation maps of a given subject and try produce a brain map that corresponds to the discriminative pattern that makes the decoding of the two conditions.">
 
 .. only:: html
 
-  .. image:: /auto_examples/images/thumb/sphx_glr_plot_variable_importance_classif_thumb.png
+  .. image:: /auto_examples/images/thumb/sphx_glr_plot_fmri_data_example_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py`
+  :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Measuring variable importance in classification</div>
+      <div class="sphx-glr-thumbnail-title">Support recovery on fMRI data</div>
     </div>
 
 
@@ -133,8 +133,8 @@ Examples Gallery
    /auto_examples/plot_knockoff_aggregation
    /auto_examples/plot_diabetes_variable_importance_example
    /auto_examples/plot_2D_simulation_example
-   /auto_examples/plot_fmri_data_example
    /auto_examples/plot_variable_importance_classif
+   /auto_examples/plot_fmri_data_example
 
 
 .. only:: html

diff --git a/docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt b/docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt
@@ -554,7 +554,7 @@ randomization.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 5.593 seconds)
+   **Total running time of the script:** (1 minutes 5.804 seconds)
 
 **Estimated memory usage:**  722 MB
 

diff --git a/docs/_sources/auto_examples/plot_dcrt_example.rst.txt b/docs/_sources/auto_examples/plot_dcrt_example.rst.txt
@@ -162,7 +162,7 @@ Plotting the comparison
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 2.729 seconds)
+   **Total running time of the script:** (1 minutes 2.893 seconds)
 
 **Estimated memory usage:**  658 MB
 

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
@@ -491,7 +491,7 @@ Analyze the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.186 seconds)
+   **Total running time of the script:** (0 minutes 8.329 seconds)
 
 **Estimated memory usage:**  643 MB
 

diff --git a/docs/_sources/auto_examples/plot_fmri_data_example.rst.txt b/docs/_sources/auto_examples/plot_fmri_data_example.rst.txt
@@ -79,7 +79,7 @@ Imports needed for this script
     from sklearn.svm import LinearSVR
     from sklearn.utils import Bunch
 
-    from hidimstat.ada_svr import ada_svr
+    from hidimstat.adaptative_permutation_threshold_SVR import ada_svr
     from hidimstat.clustered_inference import clustered_inference
     from hidimstat.ensemble_clustered_inference import ensemble_clustered_inference
     from hidimstat.permutation_test import permutation_test, permutation_test_pval
@@ -179,15 +179,25 @@ 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. (0 seconds, 0 min)
+    [fetch_single_file]  ...done. (1 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 94011392 of 291168628 bytes (32.3%%,    2.1s remaining)
-    [_chunk_report_]     Downloaded 212819968 of 291168628 bytes (73.1%%,    0.7s remaining)
-    [fetch_single_file]  ...done. (3 seconds, 0 min)
+    [_chunk_report_]     Downloaded 15261696 of 291168628 bytes (5.2%%,   18.2s remaining)
+    [_chunk_report_]     Downloaded 53190656 of 291168628 bytes (18.3%%,    9.1s remaining)
+    [_chunk_report_]     Downloaded 91701248 of 291168628 bytes (31.5%%,    6.6s remaining)
+    [_chunk_report_]     Downloaded 129933312 of 291168628 bytes (44.6%%,    5.0s remaining)
+    [_chunk_report_]     Downloaded 156491776 of 291168628 bytes (53.7%%,    4.4s remaining)
+    [_chunk_report_]     Downloaded 178135040 of 291168628 bytes (61.2%%,    3.9s remaining)
+    [_chunk_report_]     Downloaded 196550656 of 291168628 bytes (67.5%%,    3.4s remaining)
+    [_chunk_report_]     Downloaded 213925888 of 291168628 bytes (73.5%%,    2.9s remaining)
+    [_chunk_report_]     Downloaded 232153088 of 291168628 bytes (79.7%%,    2.3s remaining)
+    [_chunk_report_]     Downloaded 250953728 of 291168628 bytes (86.2%%,    1.6s remaining)
+    [_chunk_report_]     Downloaded 270131200 of 291168628 bytes (92.8%%,    0.9s remaining)
+    [_chunk_report_]     Downloaded 289538048 of 291168628 bytes (99.4%%,    0.1s remaining)
+    [fetch_single_file]  ...done. (12 seconds, 0 min)
 
     [uncompress_file] Extracting data from /home/runner/nilearn_data/haxby2001/9cabe068089e791ef0c5fe930fc20e30/subj2-2010.01.14.tar.gz...
     [uncompress_file] .. done.
@@ -375,7 +385,7 @@ 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:   32.0s finished
+    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   34.1s finished
 
 
 
@@ -620,9 +630,9 @@ spurious discoveries.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 23.795 seconds)
+   **Total running time of the script:** (1 minutes 37.697 seconds)
 
-**Estimated memory usage:**  3287 MB
+**Estimated memory usage:**  3398 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 40.613 seconds)
+   **Total running time of the script:** (5 minutes 39.238 seconds)
 
-**Estimated memory usage:**  841 MB
+**Estimated memory usage:**  793 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
@@ -176,7 +176,7 @@ Visualize the data
  .. code-block:: none
 
 
-    [<matplotlib.axis.YTick object at 0x7fef2ae3e660>, <matplotlib.axis.YTick object at 0x7fef2ae3eae0>, <matplotlib.axis.YTick object at 0x7fef1c38e780>, <matplotlib.axis.YTick object at 0x7fef1c38e660>, <matplotlib.axis.YTick object at 0x7fef1c38e990>, <matplotlib.axis.YTick object at 0x7fef1c38e060>, <matplotlib.axis.YTick object at 0x7fef2aee7710>, <matplotlib.axis.YTick object at 0x7fef2aee4aa0>, <matplotlib.axis.YTick object at 0x7fef2aee48c0>, <matplotlib.axis.YTick object at 0x7fef1c38c7d0>]
+    [<matplotlib.axis.YTick object at 0x7f5052d70ad0>, <matplotlib.axis.YTick object at 0x7f514c368b00>, <matplotlib.axis.YTick object at 0x7f514c2b5b50>, <matplotlib.axis.YTick object at 0x7f514c2b5bb0>, <matplotlib.axis.YTick object at 0x7f514c36a5d0>, <matplotlib.axis.YTick object at 0x7f5067066810>, <matplotlib.axis.YTick object at 0x7f5067067500>, <matplotlib.axis.YTick object at 0x7f5067065760>, <matplotlib.axis.YTick object at 0x7f5064899970>, <matplotlib.axis.YTick object at 0x7f5067065d60>]
 
 
 
@@ -283,13 +283,6 @@ estimate the importance of the features.
 
 
 
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
-      warnings.warn(
-
 
 
 
@@ -422,16 +415,16 @@ the features.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fef2ae82540>
+    <matplotlib.legend.Legend object at 0x7f505c767590>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 42.709 seconds)
+   **Total running time of the script:** (0 minutes 33.797 seconds)
 
-**Estimated memory usage:**  639 MB
+**Estimated memory usage:**  640 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
 =================
-**10:03.624** total execution time for 6 files **from auto_examples**:
+**10:07.758** 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``)
-     - 05:40.613
-     - 841.3
+     - 05:39.238
+     - 793.3
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``plot_fmri_data_example.py``)
-     - 01:23.795
-     - 3287.3
+     - 01:37.697
+     - 3398.5
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``plot_2D_simulation_example.py``)
-     - 01:05.593
-     - 722.1
+     - 01:05.804
+     - 722.0
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``plot_dcrt_example.py``)
-     - 01:02.729
-     - 658.2
+     - 01:02.893
+     - 657.6
    * - :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py` (``plot_variable_importance_classif.py``)
-     - 00:42.709
-     - 639.3
+     - 00:33.797
+     - 639.7
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``plot_diabetes_variable_importance_example.py``)
-     - 00:08.186
-     - 643.0
+     - 00:08.329
+     - 642.8
diff --git a/docs/_sources/generated/hidimstat.ada_svr.rst.txt b/docs/_sources/generated/hidimstat.ada_svr.rst.txt
diff --git a/docs/_sources/sg_execution_times.rst.txt b/docs/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**10:03.624** total execution time for 6 files **from all galleries**:
+**10:07.758** 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``)
-     - 05:40.613
-     - 841.3
+     - 05:39.238
+     - 793.3
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``../examples/plot_fmri_data_example.py``)
-     - 01:23.795
-     - 3287.3
+     - 01:37.697
+     - 3398.5
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``../examples/plot_2D_simulation_example.py``)
-     - 01:05.593
-     - 722.1
+     - 01:05.804
+     - 722.0
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``../examples/plot_dcrt_example.py``)
-     - 01:02.729
-     - 658.2
+     - 01:02.893
+     - 657.6
    * - :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py` (``../examples/plot_variable_importance_classif.py``)
-     - 00:42.709
-     - 639.3
+     - 00:33.797
+     - 639.7
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``../examples/plot_diabetes_variable_importance_example.py``)
-     - 00:08.186
-     - 643.0
+     - 00:08.329
+     - 642.8
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+g349ade8',
+    VERSION: '0.1.dev1+g635d003',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',

diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
@@ -513,9 +513,11 @@ const Search = {
     // perform the search on the required terms
     searchTerms.forEach((word) => {
       const files = [];
+      // find documents, if any, containing the query word in their text/title term indices
+      // use Object.hasOwnProperty to avoid mismatching against prototype properties
       const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
+        { files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term },
+        { files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, score: Scorer.title },
       ];
       // add support for partial matches
       if (word.length > 2) {
@@ -547,8 +549,9 @@ const Search = {
 
         // set score for the word in each file
         recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
+          if (!scoreMap.has(file)) scoreMap.set(file, new Map());
+          const fileScores = scoreMap.get(file);
+          fileScores.set(word, record.score);
         });
       });
 
@@ -587,7 +590,7 @@ const Search = {
         break;
 
       // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w)));
       // add result to the result list
       results.push([
         docNames[file],