Metadata-Version: 2.1
Name: mvem
Version: 0.1.1
Summary: UNKNOWN
Home-page: https://github.com/krisskul/multivariate_em
Author: Kristoffer Skuland
Author-email: kristoffer.skuland@gmail.com
License: wtfpl
Description: # Maximum Likelihood Estimation in Multivariate Probability Distributions Using EM Algorithms
        
        **mvem** is a Python package that provides maximum likelihood estimation methods for multivariate probability distributions using expectation–maximization (EM) algorithms. Additionally, it includes some functionality for fitting non-Gaussian multivariate mixture models. For fitting a wide range of univariate probability distributions, we refer to `scipy.stats`.
        
        Currently included:
        - normal (`mvem.stats.multivariate_norm`)
        - skew normal (`mvem.stats.multivariate_skewnorm`)
        - Student's *t* (`mvem.stats.multivariate_t`)
        - normal-inverse Gaussian (`mvem.stats.multivariate_norminvgauss`)
        - generalised hyperbolic skew Student's *t* (`mvem.stats.multivariate_genskewt`)
        - generalised hyperbolic (`mvem.stats.multivariate_genhyperbolic`)
        - hyperbolic (`mvem.stats.multivariate_hyperbolic`)
        - variance-gamma (`mvem.stats.multivariate_vargamma`)
        
        Multivariate mixture models currently included:
        - skew normal (`mvem.mixture.skewnorm`)
        
        ## Where to get it
        The source code is currently hosted on GitHub at: https://github.com/krisskul/mvem
        
        Binary installers for the latest released version are available at the [Python Package Index (PyPI)](https://pypi.org/project/mvem).
        
        ```sh
        pip install mvem
        ```
        
        ## Quickstart
        
        ```
        from mvem.stats import multivariate_norminvgauss
        # assume p-variate data x
        params = multivariate_norminvgauss.fit(x)
        ```
        
        ## Requirements
        
        ```
        numpy
        scikit-learn
        scipy>=1.6
        ```
Keywords: Maximum likelihood parameter estimation in multivariate distributions using EM algorithms
Platform: UNKNOWN
Description-Content-Type: text/markdown
