zeus: A Python implementation of Ensemble Slice Sampling for efficient
  Bayesian parameter inference
  We introduce zeus, a well-tested Python implementation of the Ensemble Slice
Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov
chain Monte Carlo (MCMC) algorithm specifically designed to tackle the
computational challenges posed by modern astronomical and cosmological
analyses. In particular, the method requires only minimal hand--tuning of 1-2
hyper-parameters that are often trivial to set; its performance is insensitive
to linear correlations and it can scale up to 1000s of CPUs without any extra
effort. Furthermore, its locally adaptive nature allows to sample efficiently
even when strong non-linear correlations are present. Lastly, the method
achieves a high performance even in strongly multimodal distributions in high
dimensions. Compared to emcee, a popular MCMC sampler, zeus performs 9 and 29
times better in a cosmological and an exoplanet application respectively.
