Metadata-Version: 2.1
Name: PyRKHSstats
Version: 2.1.0
Summary: A Python package for kernel methods in Statistics/ML.
Home-page: https://github.com/Black-Swan-ICL/PyRKHSstats
Author: K. M-H
Author-email: kmh.pro@protonmail.com
License: GNU General Public License v3.0
Description: # PyRKHSstats
        A Python package implementing a variety of statistical/machine learning methods 
        that rely on kernels (e.g. HSIC for independence testing).
        
        ## Overview
        - Independence testing with HSIC (Hilbert-Schmidt Independence Criterion), as 
          introduced in
          [A Kernel Statistical Test of Independence](https://papers.nips.cc/paper/2007/hash/d5cfead94f5350c12c322b5b664544c1-Abstract.html), 
          A. Gretton, K. Fukumizu, C. Hui Teo, L. Song, B. Sch&#246;lkopf, and A. 
          Smola (NIPS 2007).
        - Measurement of conditional independence with HSCIC (Hilbert-Schmidt 
          Conditional Independence Criterion), as introduced in 
          [A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings](https://papers.nips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html),
          J. Park and K. Muandet (NeurIPS 2020).
        - The Kernel-based Conditional Independence Test (KCIT), as introduced in 
          [Kernel-based Conditional Independence Test and Application in Causal 
          Discovery](https://arxiv.org/abs/1202.3775), K. Zhang, J. Peters, D. Janzing,
          B. Sch&#246;lkopf (UAI 2011).
        - Two-sample testing (also known as homogeneity testing) with the MMD 
          (Maximum Mean Discrepancy), as presented in [A Fast, Consistent Kernel 
          Two-Sample Test](https://papers.nips.cc/paper/2009/hash/9246444d94f081e3549803b928260f56-Abstract.html),
          A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) 
          and in [A Kernel Two-Sample Test](https://jmlr.org/papers/v13/gretton12a.html), 
          A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Sch&#246;lkopf, and A. Smola 
          (JMLR, volume 13, 2012).
        
        <br>
        
        | Resource | Description | 
        | :---  | :--- | 
        | HSIC | For independence testing | 
        | HSCIC | For the measurement of conditional independence | 
        | KCIT | For conditional independence testing | 
        | MMD | For two-sample testing |
        
        
        ## Implementations available
        
        The following table details the implementation schemes for the different 
        resources available in the package.
        
        | Resource | Implementation Scheme | Numpy based available | PyTorch based available |
        | :---  | :--- | :----: |:----: |
        | HSIC | Resampling (permuting the x<sub>i</sub>'s but leaving the y<sub>i</sub>'s unchanged) | Yes | No |
        | HSIC | Gamma approximation | Yes | No |
        | HSCIC | N/A | Yes | Yes |
        | KCIT | Gamma approximation | Yes | No |
        | KCIT | Monte Carlo simulation (weighted sum of &chi;<sup>2</sup> random variables)| Yes | No |
        | MMD | Gram matrix spectrum | Yes | No |
        
        [comment]: <> (| MMD | Permutation | Yes | No |)
        
        <br>
        
        ## In development
        - Joint independence testing with dHSIC.
        - Goodness-of-fit testing.
        - Methods for time series models.
        - Bayesian statistical kernel methods.
        - Regression by independence maximisation.
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Financial and Insurance Industry
Requires-Python: >=3.8
Description-Content-Type: text/markdown
