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        "description": "Markov chain Monte Carlo (MCMC) estimation provides a solution to the\ncomplex integration problems that are faced in the Bayesian analysis\nof statistical problems.  The implementation of MCMC algorithms is,\nhowever, code intensive and time consuming.  We have developed a\nPython package, which is called PyMCMC, that aids in the construction\nof MCMC samplers and helps to substantially reduce the likelihood of\ncoding error, as well as aid in the minimisation of repetitive code.\nPyMCMC contains classes for Gibbs, Metropolis Hastings, independent\nMetropolis Hastings, random walk Metropolis Hastings, orientational\nbias Monte Carlo and slice samplers as well as specific modules for\ncommon models such as a module for Bayesian regression analysis.\nPyMCMC is straightforward to optimise, taking advantage of the Python\nlibraries Numpy and Scipy, as well as being readily extensible with C\nor Fortran.\n\n",
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        "license": "GNU GPLv3",
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