LEO-Py: Estimating likelihoods for correlated, censored, and uncertain
  data with given marginal distributions
  Data with uncertain, missing, censored, and correlated values are commonplace
in many research fields including astronomy. Unfortunately, such data are often
treated in an ad hoc way in the astronomical literature potentially resulting
in inconsistent parameter estimates. Furthermore, in a realistic setting, the
variables of interest or their errors may have non-normal distributions which
complicates the modeling. I present a novel approach to compute the likelihood
function for such data sets. This approach employs Gaussian copulas to decouple
the correlation structure of variables and their marginal distributions
resulting in a flexible method to compute likelihood functions of data in the
presence of measurement uncertainty, censoring, and missing data. I demonstrate
its use by determining the slope and intrinsic scatter of the star forming
sequence of nearby galaxies from observational data. The outlined algorithm is
implemented as the flexible, easy-to-use, open-source Python package LEO-Py.
