Package: hidimstat
HiDimStat: High-dimensional statistical inference tool for PythonThe HiDimStat package provides statistical inference methods to solve the
problem of support recovery in the context of high-dimensional and
spatially structured data.InstallationHiDimStat working only with Python 3, ideally Python 3.6+. For installation,
run the following from terminalpipinstallhidimstatOr if you want the latest version available (for example to contribute to
the development of this project:gitclonehttps://github.com/ja-che/hidimstat.gitcdhidimstat
pipinstall-e.Dependenciesjoblib
numpy
scipy
scikit-learnTo run examples it is neccessary to installmatplotlib, and to run tests it
is also needed to installpytest.Documentation & ExamplesAll the documentation of HiDimStat is available athttps://ja-che.github.io/hidimstat/.As of now in theexamplesfolder there are three Python scripts that
illustrate how to use the main HiDimStat functions.
In each script we handle a different kind of dataset:plot_2D_simulation_example.pyhandles a simulated dataset with a 2D
spatial structure,plot_fmri_data_example.pysolves the decoding problem on Haxby fMRI dataset,plot_meg_data_example.pytackles the source localization problem on several
MEG/EEG datasets.# For example run the following command in terminalpythonplot_2D_simulation_example.pyReferencesThe algorithms developed in this package have been detailed in several
conference/journal articles that can be downloaded athttps://ja-che.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. InInternational 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. InProceedings 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. InNeuroImage, 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. InProceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.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/1438606853Candès, 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.LicenseThis project is licensed under the BSD 2-Clause License.AcknowledgmentsThis project has been funded by Labex DigiCosme (ANR-11-LABEX-0045-DIGICOSME)
as part of the program "Investissement d’Avenir" (ANR-11-IDEX-0003-02), by the
Fast Big project (ANR-17-CE23-0011) and the KARAIB AI Chair
(ANR-20-CHIA-0025-01). This study has also been supported by the European
Union’s Horizon 2020 research and innovation program
(Grant Agreement No. 945539, Human Brain Project SGA3).