cscherrer

using MeasureTheory
using MaskArrays
using Soss
using TransformVariables

a = rand(Cauchy() ^ 10)
b = rand(Cauchy() ^ 10)
x = a' .+ b .+ randn(10,10)

cscherrer

using Soss
using MeasureTheory

minit = @model begin
    s ~ Poisson(300_000_000)
    i ~ Dirac(1)
    r ~ Dirac(0)
    d ~ Dirac(0)
end

cscherrer

using DynamicIterators
using MeasureTheory
using ConcreteStructs

abstract type Step end

struct Sample <: Step end

sample = Sample()

cscherrer

julia> using StructArrays

julia> using ForwardDiff

julia> function partiali(n,i)
           ith = zeros(n)
           ith[i] += 1
           function (f,x)
               sa = StructArray{ForwardDiff.Dual{}}((x, ith))
               return f(sa)

cscherrer

using IRTools: @dynamo, IR, xcall, arguments, insertafter!, recurse!, self

mutable struct CallStack
    count::Int
end

inc!(d::CallStack) = d.count += 1
dec!(d::CallStack) = d.count -= 1

function hook0(callstack::CallStack, f, args...)

cscherrer

# This file is machine-generated - editing it directly is not advised

[[AbstractAlgebra]]
deps = ["InteractiveUtils", "LinearAlgebra", "Markdown", "Random", "RandomExtensions", "SparseArrays", "Test"]
git-tree-sha1 = "7df2949bfd757e426897a4b579fbd5dc776ff8c9"
uuid = "c3fe647b-3220-5bb0-a1ea-a7954cac585d"
version = "0.12.0"

[[AbstractLattices]]
git-tree-sha1 = "f35684b7349da49fcc8a9e520e30e45dbb077166"

cscherrer

using Soss
using Distributions

mstep = @model pars,state begin
    # Parameters
    α = pars.α        # Daily transmission rate
    βγ = pars.βγ        # Daily recovery rate, case fatality rate
    
    # Starting counts
    s0 = state.s      # Susceptible

cscherrer

JuliaActuary/MortalityTables.jl
JuliaActuary/DynamicPolynomials.jl
JuliaActuary/FixedPolynomials.jl
JuliaActuary/MultivariateBases.jl
JuliaActuary/MultivariateMoments.jl
JuliaActuary/MultivariatePolynomials.jl
JuliaActuary/SemialgebraicSets.jl
JuliaActuary/StaticPolynomials.jl
JuliaActuary/TypedPolynomials.jl

cscherrer

import PyCall,SymPy
using MLStyle

stats = PyCall.pyimport_conda("sympy.stats", "sympy");
SymPy.import_from(stats)

sym(x) = SymPy.symbols(x);

macro ℓ(expr)
    args = @match expr begin

cscherrer

using Transducers
T = Transducers

struct MeanVar <: Transducer
end


function T.start(rf::T.R_{MeanVar}, result)
    private_state = (0, 0.0, 0.0)