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

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?

SciML / SciMLExpectations.jl

What is the difference between `using IntegralCubature` and `using Integrals, Cubature` in the example? #152