Metadata-Version: 2.1
Name: copulae
Version: 0.7.1
Summary: Python copulae library for dependency modelling
Home-page: UNKNOWN
Author: Daniel Bok
Author-email: daniel.bok@outlook.com
Maintainer: Daniel Bok
Maintainer-email: daniel.bok@outlook.com
License: MIT
Project-URL: Documentation, https://copulae.readthedocs.io/en/latest/
Project-URL: Code, https://github.com/DanielBok/copulae
Project-URL: Issue tracker, https://github.com/DanielBok/copulae/issues
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. 
        
        ###### Versions
        
        [![Anaconda Version](https://anaconda.org/conda-forge/copulae/badges/version.svg)](https://anaconda.org/conda-forge/copulae/badges/version.svg)
        [![PyPI version](https://badge.fury.io/py/copulae.svg)](https://badge.fury.io/py/copulae)
        
        ###### Continuous Integration
        
        [![Build Status](https://travis-ci.com/DanielBok/copulae.svg?branch=master)](https://travis-ci.com/DanielBok/copulae) 
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/copulae/badges/latest_release_date.svg)](https://anaconda.org/conda-forge/copulae)
        [![Downloads](https://pepy.tech/badge/copulae)](https://pepy.tech/project/copulae)
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/copulae/badges/downloads.svg)](https://anaconda.org/conda-forge/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/) and on conda.
        
        ```bash
        # conda
        conda install -c conda-forge copulae 
        
        ```
        
        ```bash
        # PyPI
        pip install -U copulae
        ```
        
        ## 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
        p = cop.params
        # cop.params = ...  # you can override parameters too, even after it's fitted!  
        
        # get a summary of the copula. If it's fitted, fit details will be present too
        cop.summary()
        
        # overriding parameters, for Elliptical Copulae, you can override the correlation matrix
        cop[:] = np.eye(8)  # in this case, this will be equivalent to an Independent Copula
        ```
        
        Most of the copulae work roughly the same way. They share pretty much the same API. The difference lies in the way they are parameterized. Read the docs to learn more about them. 😊
        
        
        ## 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. 😊
        
        ## TODOS
        
        - [x] Set up package for pip and conda installation
        - [ ] More documentation on usage and post docs on rtd (Permanently in the works 😊)
        - [x] Elliptical Copulas
            - [x] Gaussian (Normal)
            - [x] Student (T)
        - [ ] Implement in Archimedean copulas
            - [x] Clayton
            - [x] Gumbel
            - [x] Frank
            - [x] Empirical
            - [ ] Joe
            - [ ] AMH 
            - [ ] Rho finding via Cubatures
        - [ ] Mixture copulas
            - [X] Gaussian Mixture Copula
            - [ ] Generic Mixture Copula
            - [x] Marginal Copula
        - [ ] Vine Copulas
        - [ ] Copulae Tests
            - [x] Radial Symmetry
            - [x] Exchangeability
            - [ ] Goodness of Fit
                - [ ] Pairwise Rosenblatt
                - [ ] Multi-Independence
                - [x] General GOF
            - [ ] Model Selection
                - [ ] Cross-Validated AIC/BIC
        
Keywords: copula,copulae,dependency modelling,dependence structures,archimdean,elliptical,finance
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
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: Programming Language :: Python :: 3.8
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
