You're welcome. It's not a matter of not being able to, it is a matter of Julia being slower than Fortran. I needed performant real-time Bayesian Blocks histograms, no ifs no buts.
Here is the original Julia package: https://github.com/francescoalemanno/BayesHistogram.jl
And here is the develop branch of my Julia XWEBQL application that calls Fortran (the fast Fortran stuff hasn't been merged into master yet): https://github.com/jvo203/XWEBQL/tree/develop
Inside you will find a "Fortran" folder with the source code "fbh.f90". Then in the "Julia" folder there is a glue Julia code for compiling and linking Fortran automatically at startup:
https://github.com/jvo203/XWEBQL/blob/develop/Julia/fortran_toolchain.jl
and then https://github.com/jvo203/XWEBQL/blob/develop/Julia/FORTRAN.jl .
Finally a Fortran-Julia interface inside xevent.jl. The memory is dynamically allocated in Fortran via C-style pointers, then dereferenced inside Julia, plus released by calling a Fortran subroutine to delete blocks (release the Fortran memory) after use.
include("FORTRAN.jl")

struct FastBayesHistogram
    edges::Ptr{Float32}
    centers::Ptr{Float32}
    widths::Ptr{Float32}
    heights::Ptr{Float32}
    n::Cint
end

FastBayesHistogram(hist::Ptr{FastBayesHistogram}) = unsafe_load(hist)

function FastBayesianBinning(x::Vector{Float32}, n::Integer, resolution::Integer=512)
    return ccall(
        fast_bayesian_binning_fptr,
        Ptr{FastBayesHistogram},
        (Ref{Float32}, Ref{Clonglong}, Ref{Cint}),
        x,
        Int64(n),
        Int32(resolution),
    )
end

function ParallelBayesianBinning(x::Vector{Float32}, n::Integer, resolution::Integer=512)
    return ccall(
        parallel_bayesian_binning_fptr,
        Ptr{FastBayesHistogram},
        (Ref{Float32}, Ref{Clonglong}, Ref{Cint}),
        x,
        Int64(n),
        Int32(resolution),
    )
end

function FastBayesianBinningEnergyRange(
    x::Vector{Float32},
    n::Integer,
    emin::Float32,
    emax::Float32,
    resolution::Integer=512,
)
    return ccall(
        fast_bayesian_binning_energy_range_fptr,
        Ptr{FastBayesHistogram},
        (Ref{Float32}, Ref{Clonglong}, Ref{Cfloat}, Ref{Cfloat}, Ref{Cint}),
        x,
        Int64(n),
        emin,
        emax,
        Int32(resolution),
    )
end

function DeleteBlocks(ptr::Ptr{FastBayesHistogram})
    return ccall(delete_blocks_fptr, Nothing, (Ref{FastBayesHistogram},), ptr)
end