  Searches for continuous gravitational waves from unknown neutron stars are
limited in sensitivity due to their high computational cost. For this reason,
developing new methods or improving existing ones can increase the probability
of making a detection. In this paper we present a new framework that uses MCMC
or nested sampling methods to follow-up candidates of continuous
gravitational-wave searches. This framework aims to go beyond the capabilities
of PYFSTAT (which is limited to the PTEMCEE sampler), by allowing a flexible
choice of sampling algorithm (using BILBY as a wrapper) and multi-dimensional
correlated prior distributions. We show that MCMC and nested sampling methods
can recover the maximum posterior point for much bigger parameter-space regions
than previously thought (including for sources in binary systems), and we
present tests that examine the capabilities of the new framework: a comparison
between the DYNESTY, NESSAI, and PTEMCEE samplers, the usage of correlated
priors, and its improved computational cost.
