Metadata-Version: 2.1
Name: chi2comb
Version: 0.1.0
Summary: Linear combination of independent noncentral chi-squared random variables
Home-page: https://github.com/limix/chi2comb-py
Author: Danilo Horta
Author-email: horta@ebi.ac.uk
Maintainer: Danilo Horta
Maintainer-email: horta@ebi.ac.uk
License: MIT
Download-URL: https://github.com/limix/chi2comb-py
Description: # chi2comb
        
        [![Travis](https://img.shields.io/travis/com/limix/chi2comb-py.svg?style=flat-square&label=linux%20%2F%20macos%20build)](https://travis-ci.com/limix/chi2comb-py) [![AppVeyor](https://img.shields.io/appveyor/ci/Horta/chi2comb-py.svg?style=flat-square&label=windows%20build)](https://ci.appveyor.com/project/Horta/chi2comb-py)
        
        This package estimates cumulative density functions of linear combinations of
        independent noncentral χ² random variables and a standard Normal distribution.
        Formally, it estimates P[Q<q], where:
        
            Q = λ₁X₁ + ... + λₙXₙ + σX₀.
        
        Xᵢ (𝚒≠𝟶) is an independent random variable following a noncentral χ² distribution with
        nᵢ degrees of freedom and noncentrality parameter λᵢ.
        X₀ is an independent random variable having a standard Normal distribution.
        
        ## Install
        
        It can be installed using the pip command
        
        ```bash
        pip install chi2comb
        ```
        
        ## Usage
        
        
        Consider the following linear combination of four random variables:
        
            Q = 6⋅X₁ + 3⋅X₂ + 1⋅X₃ + 2⋅X₀,
        
        where X₁, X₂, and X₃ are noncentral χ² random variables having degrees of freedom
        n₁=n₂=1 and n₃=2 and noncentrality parameters λ₁=0.5 and λ₂=λ₃=0.
        Let us estimate P[Q<1]:
        
        ```python
        >>> from chi2comb import chi2comb_cdf, ChiSquared
        >>>
        >>> gcoef = 2
        >>> ncents = [0.5, 0, 0]
        >>> q = 1
        >>> dofs = [1, 1, 2]
        >>> coefs = [6, 3, 1]
        >>> chi2s = [ChiSquared(coefs[i], ncents[i], dofs[i]) for i in range(3)]
        >>> result, errno, info = chi2comb_cdf(q, chi2s, gcoef)
        >>> result
        0.050870657088644244
        >>> errno
        0
        >>> info
        Info(emag=0.6430413191446991, niterms=43, nints=1, intv=0.03462571527167856, truc=1.4608856930426104, sd=0.0, ncycles=21)
        ```
        
        The estimated value is P[Q<1] ≈ 0.0587.
        
        ## Problems
        
        If you encounter any issue, please, [submit it](https://github.com/limix/chi2comb-py/issues/new).
        
        ## Authors
        
        * [Danilo Horta](https://github.com/horta)
        
        ## License
        
        This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/chi2comb-py/master/LICENSE.md).
        
Keywords: chi-squared,probability,distribution
Platform: Windows
Platform: MacOS
Platform: Linux
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
