dyPolyChord: dynamic nested sampling with PolyChord. Nested sampling is a numerical method for Bayesian computation which simultaneously calculates posterior samples and an estimate of the Bayesian evidence for a given likelihood and prior. The approach is popular in scientific research, and performs well compared to Markov chain Monte Carlo (MCMC)-based sampling for multi-modal or degenerate posterior distributions. dyPolyChord implements dynamic nested sampling using the efficient PolyChord sampler to provide state-of-the-art nested sampling performance. Any likelihoods and priors which work with PolyChord can be used (Python, C++ or Fortran), and the output files produced are in the PolyChord format.