  Probabilistic programming and statistical computing are vibrant areas in the
development of the Julia programming language, but the underlying
infrastructure dramatically predates recent developments. The goal of
MeasureTheory.jl is to provide Julia with the right vocabulary and tools for
these tasks.
  In the package we introduce a well-chosen set of notions from the foundations
of probability together with powerful combinators and transforms, giving a
gentle introduction to the concepts in this article.
  The task is foremost achieved by recognizing measure as the central object.
This enables us to develop a proper concept of densities as objects relating
measures with each others. As densities provide local perspective on measures,
they are the key to efficient implementations.
  The need to preserve this computationally so important locality leads to the
new notion of locally-dominated measure solving the so-called base measure
problem and making work with densities and distributions in Julia easier and
more flexible.
