EPIC - Easy Parameter Inference in Cosmology: The user's guide to the
  MCMC sampler
  Easy Parameter Inference in Cosmology (EPIC) is another Markov Chain Monte
Carlo (MCMC) sampler for Cosmology. It is implemented in Python and provides
Bayesian parameter inference and model comparison based on the Bayesian
evidence. The Parallel Tempering algorithm is included, which can help in the
exploration of posterior distributions with two or more separated peaks.
Adaptive routines for obtaining better efficiency with fine-tuned algorithms
are being developed and will be available in future versions. In this user's
guide, I give general instructions for installation and usage, including
examples, and show how to modify the code in order to add new datasets and
models.
