Metadata-Version: 2.1
Name: copulae
Version: 0.2.2
Summary: Python copulae library for dependency modelling
Home-page: https://github.com/DanielBok/copulae
Author: Daniel Bok
Author-email: daniel.bok@outlook.com
Maintainer: Daniel Bok
Maintainer-email: daniel.bok@outlook.com
License: MIT
Description: # Copulae
        
        Copulae is a package used to model complex dependency structures. Copulae implements common and
        popular copula structures to bind multiple univariate streams of data together. All copula 
        implemented are multivariate by default. 
        
        ###### Continuous Integration
        
        [![Build Status](https://travis-ci.com/DanielBok/copulae.svg?branch=master)](https://travis-ci.com/DanielBok/copulae)
        
        ###### Documentation
        
        [![Documentation Status](https://readthedocs.org/projects/copulae/badge/?version=latest)](https://copulae.readthedocs.io/en/latest/?badge=latest)
        
        ###### Coverage
        
        [![Coverage Status](https://coveralls.io/repos/github/DanielBok/copulae/badge.svg?branch=master)](https://coveralls.io/github/DanielBok/copulae?branch=master)
        
        ## Installing
        
        Install and update using [pip](https://pip.pypa.io/en/stable/quickstart/)
        
        ```bash
        pip install -U copulae
        ```
        
        Still working on the conda build. Please wait a while more!!  
        
        ## Documentation
        
        The documentation is located at https://copulae.readthedocs.io/en/latest/. Please check it out. :)
        
        
        ## Simple Usage
        
        ```python
        from copulae import NormalCopula
        import numpy as np
        
        np.random.seed(8)
        data = np.random.normal(size=(300, 8))
        cop = NormalCopula(8)
        cop.fit(data)
        
        cop.random(10)  # simulate random number
        
        # getting parameters
        print(cop.params)  
        
        # overriding parameters
        cop[:] = np.eye(8)  # in this case,  setting to independent Gaussian Copula
        ```
        
        I'll work on the docs and other copulas as soon as I can!
        
        
        ## Acknowledgements
        
        Most of the code has been implemented by learning from others. Copulas are not the easiest
        beasts to understand but here are some items that helped me along the way. I would recommend
        all the works listed below.
        
        #### [Elements of Copula Modeling with R](https://www.amazon.com/Elements-Copula-Modeling-Marius-Hofert/dp/3319896342/)
        
        I referred quite a lot to the textbook when first learning. The authors give a pretty thorough explanation 
        of copula from ground up. They go from describing when you can use copulas for modeling to the different 
        classes of copulas to how to fit them and more.
        
        #### [Blogpost from Thomas Wiecki](https://twiecki.io/blog/2018/05/03/copulas/) 
        
        This blogpost gives a very gentle introduction to copulas. Before diving into all the complex math you'd 
        find in textbooks, this is probably the best place to start. 
        
        
        ## Motivations
        
        I started working on the copulae package because I couldn't find a good existing package that does
        multivariate copula modeling. Presently, I'm building up the package according to my needs at work.
        If you feel that you'll need some features, you can drop me a message. I'll see how I can schedule it. ðŸ˜Š
        ## TODOS
        
        - [ ] Set up package for pip and conda installation
        - [ ] More documentation on usage and post docs on rtd
        - [ ] Elliptical Copulas
            - [x] Gaussian (Normal)
            - [x] Student (T)
        - [ ] Implement in Archmedeans copulas
            - [x] Clayton
            - [x] Gumbel
            - [ ] Frank
            - [ ] Joe
            - [ ] AMH 
        - [ ] Implement goodness of fit
        - [ ] Implement mixed copulas
        - [ ] Implement more solvers
        - [ ] Implement convenient graphing functions
        
Keywords: copula,copulae,dependency modelling,dependence structures,archimdean,elliptical,finance
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
