  We present $\texttt{XookSuut}$, a Python implementation of the
$\texttt{DiskFit}$ algorithm, optimized to perform robust Bayesian inference on
parameters describing models of circular and noncircular rotation in galaxies.
$\texttt{XookSuut}$~surges as a Bayesian alternative for kinematic modeling of
2D velocity maps; it implements efficient sampling methods, specifically Markov
Chain Monte Carlo (MCMC) and Nested Sampling (NS), to obtain the posteriors and
marginalized distributions of kinematic models including circular motions,
axisymmetric radial flows, bisymmetric flows, and harmonic decomposition of the
LoS~velocity. In this way, kinematic models are obtained by pure sampling
methods, rather than standard minimization techniques based on the $\chi^2$.
All together, $\texttt{XookSuut}$~represents a sophisticated tool for deriving
rotational curves and to explore the error distribution and covariance between
parameters.
