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        "description": "*Cobaya*, a code for Bayesian analysis in Cosmology\n===================================================\n\n:Author: `Jesus Torrado`_ and `Antony Lewis`_\n\n:Source: `Source code at GitHub <https://github.com/CobayaSampler/cobaya>`_\n\n:Documentation: `Documentation at Readthedocs <https://cobaya.readthedocs.org>`_\n\n:Licence: `LGPL <https://www.gnu.org/licenses/lgpl-3.0.en.html>`_ + mandatory bug reporting asap + mandatory `arXiv'ing <https://arxiv.org>`_ of publications using it (see `LICENCE.txt <https://github.com/CobayaSampler/cobaya/blob/master/LICENCE.txt>`_ for exceptions). The documentation is licensed under the `GFDL <https://www.gnu.org/licenses/fdl-1.3.en.html>`_.\n\n:E-mail list: https://cosmocoffee.info/cobaya/ \u2013 **sign up for important bugs and release announcements!**\n\n:Support: For general support, CosmoCoffee_; for bugs and issues, use the `issue tracker <https://github.com/CobayaSampler/cobaya/issues>`_.\n\n:Installation: ``pip install cobaya --upgrade --user`` (see the `installation instructions <https://cobaya.readthedocs.io/en/latest/installation.html>`_; in general do *not* clone)\n\n.. image:: https://secure.travis-ci.org/CobayaSampler/cobaya.png?branch=master\n   :target: https://secure.travis-ci.org/CobayaSampler/cobaya\n.. image:: https://img.shields.io/pypi/v/cobaya.svg?style=flat\n   :target: https://pypi.python.org/pypi/cobaya/\n.. image:: https://readthedocs.org/projects/cobaya/badge/?version=latest\n   :target: https://cobaya.readthedocs.org/en/latest\n\n\n\n**Cobaya** (**co**\\ de for **bay**\\ esian **a**\\ nalysis, and Spanish for *Guinea Pig*) is a framework for sampling and statistical modelling: it allows you to explore an arbitrary prior or posterior using a range of Monte Carlo samplers (including the advanced MCMC sampler from CosmoMC_, and the advanced nested sampler PolyChord_). The results of the sampling can be analysed with GetDist_. It supports MPI parallelization (and very soon HPC containerization with Docker/Shifter and Singularity).\n\nIts authors are `Jesus Torrado`_ and `Antony Lewis`_. Some ideas and pieces of code have been adapted from other codes (e.g CosmoMC_ by `Antony Lewis`_ and contributors, and `Monte Python`_, by `Julien Lesgourgues`_ and `Benjamin Audren`_).\n\n**Cobaya** has been conceived from the beginning to be highly and effortlessly extensible: without touching **cobaya**'s source code, you can define your own priors and likelihoods, create new parameters as functions of other parameters...\n\nThough **cobaya** is a general purpose statistical framework, it includes interfaces to cosmological *theory codes* (CAMB_ and CLASS_) and *likelihoods of cosmological experiments* (Planck, Bicep-Keck, SDSS... and more coming soon). Automatic installers are included for all those external modules. You can also use **cobaya** simply as a wrapper for cosmological models and likelihoods, and integrate it in your own sampler/pipeline.\n\nThe interfaces to most cosmological likelihoods are agnostic as to which theory code is used to compute the observables, which facilitates comparison between those codes. Those interfaces are also parameter-agnostic, so using your own modified versions of theory codes and likelihoods requires no additional editing of **cobaya**'s source.\n\nThe overhead per posterior evaluation is ``< 0.1 ms / dimension`` per posterior evaluation (mostly due to evaluating ``scipy.stats`` logpdf's in the prior), which makes it suitable for most cosmological applications (CAMB_ and CLASS_ take seconds to run), but not necessarily for more general statistical applications, if the evaluation time per pdf involved is of that order or smaller.\n\n\nHow to cite us\n--------------\n\nAs of this version, there is no scientific publication yet associated to this software, so simply mention its `GitHub repository <https://github.com/CobayaSampler/cobaya>`_.\n\nTo appropriately cite the modules (samplers, theory codes, likelihoods) that you have used, simply run the script `cobaya-bib` with your input file(s) as argument(s), and you will get *bibtex* references and a short suggested text snippet for each module mentioned in your input file. You can find a usage example `here <https://cobaya.readthedocs.io/en/latest/cosmo_basic_runs.html#citations>`_.\n\n\nAcknowledgements\n----------------\n\nThanks to `Julien Lesgourgues`_ and `Will Handley`_ for support on interfacing CLASS_ and PolyChord_ respectively.\n\nThanks too to `Guadalupe Ca\u00f1as Herrera`_, `Andreas Finke`_, `Lukas Hergt`_, `Vivian Miranda`_, `Timothy Morton`_, `Joe Zunz`_ and many others for extensive and somewhat painful testing.\n\n.. _`Jesus Torrado`: https://astronomy.sussex.ac.uk/~jt386\n.. _`Antony Lewis`: https://cosmologist.info\n.. _CosmoMC: https://cosmologist.info/cosmomc/\n.. _CosmoCoffee: https://cosmocoffee.info/viewforum.php?f=11\n.. _`Monte Python`: https://baudren.github.io/montepython.html\n.. _Camb: https://camb.info/\n.. _Class: https://class-code.net/\n.. _GetDist: https://github.com/cmbant/getdist\n.. _PolyChord: https://github.com/PolyChord/PolyChordLite\n.. _`Julien Lesgourgues`: https://www.particle-theory.rwth-aachen.de/cms/Particle-Theory/Das-Institut/Mitarbeiter-TTK/Professoren/~gufe/Lesgourgues-Julien/?lidx=1\n.. _`Benjamin Audren`: https://baudren.github.io/\n.. _`Guadalupe Ca\u00f1as Herrera`: https://gcanasherrera.github.io/pages/about-me.html#about-me\n.. _`Andreas Finke`: https://cosmology.unige.ch/users/andreas-finke\n.. _`Vivian Miranda`: https://github.com/vivianmiranda\n.. _`Lukas Hergt`: https://www.kicc.cam.ac.uk/directory/lh561\n.. _`Joe Zunz`: https://github.com/joezuntz\n.. _`Timothy Morton`: https://github.com/timothydmorton\n.. _`Will Handley`: https://www.kicc.cam.ac.uk/directory/wh260",
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