  Here we present juliet, a versatile tool for the analysis of transits,
radial-velocities, or both. juliet is built over many available tools for the
modelling of transits, radial-velocities and stochastic processes (here
modelled as Gaussian Processes; GPs) in order to deliver a tool/wrapper which
can be used for the analysis of transit photometry and radial-velocity
measurements from multiple instruments at the same time, using nested sampling
algorithms which allows it to not only perform a thorough sampling of the
parameter space, but also to perform model comparison via bayesian evidences.
In addition, juliet allows to fit transiting and non-transiting multi-planetary
systems, and to fit GPs which might share hyperparameters between the
photometry and radial-velocities simultaneously (e.g., stellar rotation
periods), which might be useful for disentangling stellar activity in
radial-velocity measurements. Nested Sampling, Importance Nested Sampling and
Dynamic Nested Sampling is performed with publicly available codes which in
turn give juliet multi-threading options, allowing it to scale the computing
time of complicated multi-dimensional problems. We make juliet publicly
available via GitHub.
