Complex computer models are used to estimate sustainable catches in fisheries, by finding the best values for dozens or hundreds of variables so that the models explain data such as trends in abundance and the number of fish at each age and length. Traditionally, software packages are used for this kind of model fitting, most commonly a package called AD Model Builder (ADMB), but more recently a package called Template Model Builder (TMB). These models work well when trying to find the very best values for model fitting, but may take days or weeks to estimate uncertainty using what are called Bayesian methods. Recently, a new Bayesian algorithm was developed that is much faster at solving the uncertainty problem, called the No-U-Turn-Sampler (NUTS), which is implemented in a third software package called Stan. In a new paper, the NUTS algorithm is adapted for use in ADMB and TMB, allowing all of the models already written in these packages to make full use of this faster method for assessing uncertainty. The paper finds that the speed can be comparable to Stan, and is likely to allow widespread usage of Bayesian methods in fisheries stock assessments, so that catch setting can be more precautionary when the models are more uncertain. The paper, by SAFS research scientist Cole Monnahan, and Kasper Kristensen of the Technical University of Denmark, appears in the journal PLoS One.