Metadata-Version: 2.1
Name: pycopula
Version: 0.1.1
Summary: Python copulas library for dependency modeling
Home-page: https://github.com/MaximeJumelle/PyCopula/
Author: Maxime Jumelle
Author-email: maxime@aipcloud.io
License: Apache 2
Description: [![Build Status](https://travis-ci.org/MaximeJumelle/PyCopula.svg?branch=master)](https://travis-ci.org/MaximeJumelle/PyCopula)
        # PyCopula - Dependencies with copulas
        PyCopula is an easy-to-use Python library that allows you to study random variables dependencies with copulas. It comes with useful tools and features to plot, estimate or simulate on copulas.
        
        * [Online Documentation](https://aipcloud.github.io/PyCopula/)
        
        ## Features
        PyCopula natively handle various families of copulas including :
        - Archimean Copulas
        	- Clayton
        	- Gumbel
        	- Joe
        	- Frank
        	- Ali-Mikhail-Haq
        - Elliptic Copulas
        	- Gaussien
        	- Student
        
        ### Estimation
        Three methods of estimation, based on *SciPy* numerical optimization routines, are available to provide high flexibility during fitting process.
        - MLE : Maximum Likelihood Estimation
        - IFM : Inference For Margins
        - CMLE : Canonical Maximum Likelihood Estimation
        
        ## Usage
        PyCopula was designed to provide an easy-to-use interface that does not require a lot in both programming and computing. As a result, only a few lines are needed to properly fit any copulas, as demonstrated in the following code snippet.
        ```python
        import pandas as pd
        from pycopula.copula import ArchimedeanCopula
        
        data = pd.read_csv("data/classic.csv").values[:,1:]
        
        archimedean = ArchimedeanCopula(family="gumbel", dim=2)
        archimedean.fit(data, method="cmle")
        ```
        ```console
        Archimedean Copula (gumbel) :
        *	Parameter : 1.605037
        ```
        
        ## Visualization
        
        #### 3D PDF and CDF
        
        ![Screenshot](resources/clayton_pdf_cdf.png)
        
        #### Concentration Functions
        
        ![Screenshot](resources/lower_upper_tail.png)
        
        #### Estimation
        
        #### Simulation
        
        ![Screenshot](resources/simulation_gaussian.png)
        
        ## Development
        
        Currently, there are only a few features implemented in the library, which are the basics components for copula handling :
        
        - Creating Archimedean, Gaussian and Student copulas
        - 3D plot of PDF and CDF
        - Concentration functions and visualization
        - Estimation of copulas parameters (CMLE, MLE, IFM)
        
        In the future, I plan to release the following features :
        
        - Goodness-of-fit
        - Copula selection with criterions and statistical testing
        - Examples of applications in real world with open data
        
        Also, if you are interested in the project, I would be happy to collaborate with you since there are still quite a lot of improvements needed (computation, estimation methods, visualization) and that I don't have enough time on my hands to do it quickly.
        
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
