What is the difference between using IntegralCubature and using Integrals, Cubature in the example?

[homocomputeris]

Question❓

Slightly modified example from the readme

using SciMLExpectations, OrdinaryDiffEq, Distributions, Integrals, Cubature

function eom!(du, u, p, t, A)
    du .= A * u
end

u0 = [1.0, 1.0]
tspan = (0.0, 3.0)
p = [1.0; 2.0]
A = [0.0 1.0; -p[1] -p[2]]
prob = ODEProblem((du, u, p, t) -> eom!(du, u, p, t, A), u0, tspan, p)
u0s_dist = (Uniform(1, 10), truncated(Normal(3.0, 1), 0.0, 6.0))
gd = GenericDistribution(u0s_dist...)
cov(x, u, p) = x, p

sm = SystemMap(prob, Tsit5(), save_everystep = false)

analytical = (exp(A * tspan[end]) * [mean(d) for d in u0s_dist])
analytical

g(sol, p) = sol[:, end]
exprob = ExpectationProblem(sm, g, cov, gd; nout = length(u0))
sol = solve(exprob, Koopman(); quadalg = CubatureJLh(),
            ireltol = 1e-3, iabstol = 1e-3)
sol.u # Expectation of the states 1 and 2 at the final time point

fails with

ERROR: UndefVarError: `CubatureJLh` not defined
Stacktrace:
 [1] top-level scope
   @ ~/test/test.jl:25

Integrals.jl docs say

CubatureJLh: h-Cubature from Cubature.jl. Requires using Cubature.

So, shouldn't SciMLExpectations.jl work with using Integrals, Cubature too? Or is there significant difference in the scope of application of those packages?

[ChrisRackauckas]

using IntegralCubature is the old way that was deprecated. It was removed when Julia v1.9 came out as package extensions gave an easier way to make the solver addons that would stay up to date. If you do this old way, then you'll have issues with your environment because its compats are really old. This repo needs to be updated, see #148