Announcing the recently registered Ziggurats.jl. Make very high performance random number generators for whatever distributions you like! (with some modest limitations)
The ziggurat algorithm uses a carefully constructed table of precomputed values to sample random numbers distributed according to a PDF with very high efficiency. It is widely used for high performance generation of normally distributed and exponentially distributed random numbers, including in Julia’s own Random.jl. The new package, Ziggurats.jl, automates the process of constructing the ziggurat tables so you can get the same high performance sampling from a very wide variety of distributions. The main requirements are that the PDF must be univariate, piecewise monotonic, and must not diverge to infinite density. Even discontinuous functions should work.
Example:
julia> using Ziggurats, Plots, BenchmarkTools
julia> z = ziggurat(x->10 - x + sin(x), (0, 9.7));
julia> histogram(rand(z, 10^5))
julia> @btime rand($z)
3.920 ns (0 allocations: 0 bytes)
Check out the README on GitHub for more information: Ziggurats.jl