Metadata-Version: 2.1
Name: student-mixture
Version: 0.1.1
Summary: A package for fitting a Student's t-mixture model
Home-page: https://github.com/omritomer/student_mixture
Author: Omri Tomer
Author-email: omritomer1@mail.tau.ac.il
License: MIT license
Description: # Student's t-Mixture Model
        
        A Python implementation of "Robust mixture modelling using the t distribution" (Peel & McLachlan, 2000) and extensions.
        
        # Features
        * Class StudentMixture: module for fitting a mixture of multivariate Student's t-distributions.
        * Class MultivariateT: module for using a multivariate Student's t-random variable
        * Class MultivaraiteTFit: module for fitting a multivariate Student's t-distribution. 
        
        # Installation
        With pip:
        ```
        pip install student-mixture
        ```
        From github:
        ```
        git https://github.com/omritomer/student_mixture.git
        cd student_mixture
        python setup.py build
        python setup.py install
        ```
        
        # Requirements
        * numpy==1.17.3
        * scipy==1.3.1
        * scikit-learn==0.21.3
        
        # References
        
        1. Peel, D., & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. _Statistics and computing_, _10_(4), 339-348.
        2. McLachlan, G. J., & Peel, D. (2004). _Finite mixture models_. John Wiley & Sons.
        3. McLachlan, G. J., & Krishnan, T. (2007). _The EM algorithm and extensions_ (Vol. 382). John Wiley & Sons.
        4. Genz, A., & Bretz, F. (2009). _Computation of multivariate normal and t probabilities_ (Vol. 195). Springer Science & Business Media.
        5. Genz, A. (2004). Numerical computation of rectangular bivariate and trivariate normal and t probabilities. _Statistics and Computing_, _14_(3), 251-260.
        6. Genz, A., & Bretz, F. (1999). Numerical computation of multivariate t-probabilities with application to power calculation of multiple contrasts. _Journal of Statistical Computation and Simulation_, _63_(4), 103-117.
        7. Genz, A., & Bretz, F. (2002). Comparison of methods for the computation of multivariate t probabilities. _Journal of Computational and Graphical Statistics_, _11_(4), 950-971.
        8. Kotz, S., & Nadarajah, S. (2004). _Multivariate t-distributions and their applications_. Cambridge University Press.
        
        # Citation
        If you used this package to estimate a mixture of Student's t-distributions, please cite references 1 and 2, which this package is an implementation of. 
        
        If you used this package to estimate a Student's t-distribution, please cite reference 3.
        
        <<<<<<< HEAD
        The implementations mentioned above are structurally based on scikit-learn's mixture module, so please also cite scikit-learn according to their suggested format, which can be found [here]([https://scikit-learn.org/stable/about.html#citing-scikit-learn](https://scikit-learn.org/stable/about.html#citing-scikit-learn)).
        =======
        The implementations above are structurally based on scikit-learn's mixture module, so please also cite scikit-learn according to their suggested format, which can be found [here]([https://scikit-learn.org/stable/about.html#citing-scikit-learn](https://scikit-learn.org/stable/about.html#citing-scikit-learn)).
        >>>>>>> 194301d0b20537ef19b8eeffa24feb0bcce1a646
        
        If you used the multivariate Student's t-distribution module, please cite reference 8. As this module is structurally based on scipy's stats.multivariate module, please also cite scipy according to their suggested format, which can be found [here]([https://www.scipy.org/citing.html](https://www.scipy.org/citing.html)).
        
        If you used the cumulative distribution function (CDF) for either a multivariate t-distribution or a Student's t-mixture model, please cite reference 4. In addition, for the following cases:
        
        - If your data has two or three dimensions, please cite reference 5.
        
        - If your data has four or more dimensions, please cite references 6 and 7.
        
        # Documentation
        [Student's t-Mixture Model]([https://student-mixture.readthedocs.io/](https://student-mixture.readthedocs.io/))
        
        # Authors
        Omri Tomer (omritomer1@mail.tau.ac.il)
        
        # License
        <<<<<<< HEAD
        This package is distributed under the BSD 3-Clause License. See the LICENSE file for information.
        =======
        This package is distributed under the BSD 3-Clause License. See the LICENSE file for information.
        >>>>>>> 194301d0b20537ef19b8eeffa24feb0bcce1a646
        
        
        =======
        History
        =======
        
        0.0.1 (2019-11-25)
        ------------------
        
        * First release on PyPI.
        
Keywords: student_mixture
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
Description-Content-Type: text/markdown
