Nesting sampling is a methodology for computing the evidence (a high-dimensional integration of the likelihood over the prior density), and the posteriors simultaneously. Implementation in Julia of three algorithmic variants of nested sampling: Random Staggering, Slicing, and Random Slicing, are discussed in this work. Much of this work was inspired by the Python package, dynesty, and its modular approach to nested sampling which Julia’s multiple dispatch made even more effective.