Update documentation

docs/html/.buildinfo

@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 396e225ad23ba5117c41c4a0d5affe10
+tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/html/_sources/api.rst.txt

@@ -0,0 +1,29 @@
+.. _api_documentation:
+
+=================
+API Documentation
+=================
+
+Estimators
+==========
+
+.. currentmodule:: hidimstat
+
+Functions
+=========
+
+.. autosummary::
+   :toctree: generated/
+
+   ada_svr
+   aggregate_quantiles
+   clustered_inference
+   desparsified_lasso
+   ensemble_clustered_inference
+   group_reid
+   hd_inference
+   multivariate_1D_simulation
+   permutation_test_cv
+   reid
+   standardized_svr
+   zscore_from_pval

docs/html/_sources/auto_examples/index.rst.txt

@@ -0,0 +1,40 @@
+:orphan:
+
+.. _general_examples:
+
+Examples Gallery
+================
+
+.. contents:: Contents
+   :local:
+   :depth: 3
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+.. thumbnail-parent-div-open
+
+.. thumbnail-parent-div-close
+
+.. raw:: html
+
+    </div>
+
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-gallery
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

docs/html/_sources/auto_examples/sg_execution_times.rst.txt

@@ -0,0 +1,37 @@
+
+:orphan:
+
+.. _sphx_glr_auto_examples_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.000** total execution time for 0 files **from auto_examples**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - N/A
+     - N/A
+     - N/A

docs/html/_sources/generated/hidimstat.ada_svr.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.ada\_svr
+==================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: ada_svr

docs/html/_sources/generated/hidimstat.aggregate_quantiles.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.aggregate\_quantiles
+==============================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: aggregate_quantiles

docs/html/_sources/generated/hidimstat.clustered_inference.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.clustered\_inference
+==============================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: clustered_inference

docs/html/_sources/generated/hidimstat.desparsified_lasso.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.desparsified\_lasso
+=============================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: desparsified_lasso

docs/html/_sources/generated/hidimstat.ensemble_clustered_inference.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.ensemble\_clustered\_inference
+========================================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: ensemble_clustered_inference

docs/html/_sources/generated/hidimstat.group_reid.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.group\_reid
+=====================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: group_reid

docs/html/_sources/generated/hidimstat.hd_inference.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.hd\_inference
+=======================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: hd_inference

docs/html/_sources/generated/hidimstat.multivariate_1D_simulation.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.multivariate\_1D\_simulation
+======================================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: multivariate_1D_simulation

docs/html/_sources/generated/hidimstat.permutation_test_cv.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.permutation\_test\_cv
+===============================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: permutation_test_cv

docs/html/_sources/generated/hidimstat.reid.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.reid
+==============
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: reid

docs/html/_sources/generated/hidimstat.standardized_svr.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.standardized\_svr
+===========================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: standardized_svr

docs/html/_sources/generated/hidimstat.zscore_from_pval.rst.txt

@@ -0,0 +1,6 @@
+hidimstat.zscore\_from\_pval
+============================
+
+.. currentmodule:: hidimstat
+
+.. autofunction:: zscore_from_pval

docs/html/_sources/index.rst.txt

@@ -0,0 +1,174 @@
+.. hidimstat documentation master file, created by
+   sphinx-quickstart on Fri April 23 12:22:52 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+HiDimStat: High-dimensional statistical inference tool for Python
+=================================================================
+|Build Status| |codecov|
+
+The HiDimStat package provides statistical inference methods to solve the
+problem of support recovery in the context of high-dimensional and
+spatially structured data.
+
+
+Installation
+------------
+
+HiDimStat working only with Python 3, ideally Python 3.6+. For installation,
+run the following from terminal::
+
+  pip install hidimstat
+
+Or if you want the latest version available (for example to contribute to
+the development of this project)::
+
+  git clone https://github.com/Parietal-INRIA/hidimstat.git
+  cd hidimstat
+  pip install -e .
+
+
+Dependencies
+------------
+
+HiDimStat depends on the following packages::
+
+  joblib
+  numpy
+  scipy
+  scikit-learn
+
+
+To run examples it is neccessary to install ``matplotlib``, and to run tests it
+is also needed to install ``pytest``.
+
+
+Documentation & Examples
+------------------------
+
+Documentation about the main HiDimStat functions is available
+`here <api.html>`_ and examples are available `here <auto_examples/index.html>`_.
+
+As of now, there are three different examples (Python scripts) that
+illustrate how to use the main HiDimStat functions.
+In each example we handle a different kind of dataset:
+``plot_2D_simulation_example.py`` handles a simulated dataset with a 2D
+spatial structure,
+``plot_fmri_data_example.py`` solves the decoding problem on Haxby fMRI dataset,
+``plot_meg_data_example.py`` tackles the source localization problem on several
+MEG/EEG datasets.
+
+.. code-block::
+
+  # For example run the following command in terminal
+  python plot_2D_simulation_example.py
+
+
+Build the documentation
+-----------------------
+
+To build the documentation you will need to run:
+
+.. code-block::
+
+    pip install -U sphinx_gallery sphinx_bootstrap_theme
+    cd doc
+    make html
+
+
+References
+----------
+
+The algorithms developed in this package have been detailed in several
+conference/journal articles that can be downloaded at
+`https://Parietal-INRIA.github.io/ <https://Parietal-INRIA.github.io/research.html>`_.
+
+Main references
+~~~~~~~~~~~~~~~
+
+Ensemble of Clustered desparsified Lasso (ECDL):
+
+* Chevalier, J. A., Salmon, J., & Thirion, B. (2018). Statistical inference
+  with ensemble of clustered desparsified lasso. In International Conference
+  on Medical Image Computing and Computer-Assisted Intervention
+  (pp. 638-646). Springer, Cham.
+
+* Chevalier, J. A., Nguyen, T. B., Thirion, B., & Salmon, J. (2021).
+  Spatially relaxed inference on high-dimensional linear models.
+  arXiv preprint arXiv:2106.02590.
+
+Aggregation of multiple Knockoffs (AKO):
+
+* Nguyen T.-B., Chevalier J.-A., Thirion B., & Arlot S. (2020). Aggregation
+  of Multiple Knockoffs. In Proceedings of the 37th International Conference on
+  Machine Learning, Vienna, Austria, PMLR 119.
+
+Application to decoding (fMRI data):
+
+* Chevalier, J. A., Nguyen T.-B., Salmon, J., Varoquaux, G. & Thirion, B.
+  (2021). Decoding with confidence: Statistical control on decoder maps.
+  In NeuroImage, 234, 117921.
+
+Application to source localization (MEG/EEG data):
+
+* Chevalier, J. A., Gramfort, A., Salmon, J., & Thirion, B. (2020).
+  Statistical control for spatio-temporal MEG/EEG source imaging with
+  desparsified multi-task Lasso. In Proceedings of the 34th Conference on
+  Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.
+
+Single/Group statistically validated importance using conditional permutations:
+
+* Chamma, A., Thirion, B., & Engemann, D. (2024). **Variable importance in
+high-dimensional settings requires grouping**. In Proceedings of the 38th
+Conference of the Association for the Advancement of Artificial
+Intelligence(AAAI 2024), Vancouver, Canada.
+
+* Chamma, A., Engemann, D., & Thirion, B. (2023). **Statistically Valid Variable
+Importance Assessment through Conditional Permutations**. In Proceedings of the
+37th Conference on Neural Information Processing Systems (NeurIPS 2023), New
+Orleans, USA.
+
+If you use our packages, we would appreciate citations to the relevant
+aforementioned papers.
+
+Other useful references
+~~~~~~~~~~~~~~~~~~~~~~~
+
+For de-sparsified(or de-biased) Lasso:
+
+* Javanmard, A., & Montanari, A. (2014). Confidence intervals and hypothesis
+  testing for high-dimensional regression. The Journal of Machine Learning
+  Research, 15(1), 2869-2909.
+
+* Zhang, C. H., & Zhang, S. S. (2014). Confidence intervals for low dimensional
+  parameters in high dimensional linear models. Journal of the Royal
+  Statistical Society: Series B: Statistical Methodology, 217-242.
+
+* Van de Geer, S., Bühlmann, P., Ritov, Y. A., & Dezeure, R. (2014). On
+  asymptotically optimal confidence regions and tests for high-dimensional
+  models. The Annals of Statistics, 42(3), 1166-1202.
+
+For Knockoffs Inference:
+
+* Barber, R. F; Candès, E. J. (2015). Controlling the false discovery rate
+  via knockoffs. Annals of Statistics. 43 , no. 5,
+  2055--2085. doi:10.1214/15-AOS1337. https://projecteuclid.org/euclid.aos/1438606853
+
+* Candès, E., Fan, Y., Janson, L., & Lv, J. (2018). Panning for gold: Model-X
+  knockoffs for high dimensional controlled variable selection. Journal of the
+  Royal Statistical Society Series B, 80(3), 551-577.
+
+.. |Build Status| image:: https://travis-ci.com/Parietal-INRIA/hidimstat.svg?branch=main
+   :target: https://codecov.io/gh/Parietal-INRIA/hidimstat
+
+.. |codecov| image:: https://codecov.io/gh/Parietal-INRIA/hidimstat/branch/main/graph/badge.svg
+   :target: https://codecov.io/gh/Parietal-INRIA/hidimstat
+
+
+API
+---
+
+.. toctree::
+    :maxdepth: 1
+
+    api.rst