Issues using LKJL

I came across some issues with `MeasureTheory.LKJL` while trying to use it with Soss. I'm posting the issue here because I'm not 100% sure which issues need to be resolved here or there.

```julia
using Soss, MeasureTheory, SampleChainsDynamicHMC

m = @model N begin
σ ~ HalfNormal(1) |> iid(N)
L ~ LKJL(N, 2)
ŷ ~ Normal() |> iid(N)
y = σ .* (L * ŷ) # same as y ~ MvNormal(zeros(N), σ .* (L * L') .* σ')
end
```

Drawing random samples does not work.

```julia
julia> rand(m(4))
ERROR: MethodError: no method matching distproxy(::LKJL{4, (:η,), Tuple{Int64}})
```

Distributions' `LKJ` isn't equivalent to `LKJL`, so we instead overload `rand` ourselves:

```julia
julia> using LinearAlgebra, Random

julia> function Base.rand(rng::AbstractRNG, ::Type, d::LKJL{k}) where {k}
return cholesky(rand(rng, MeasureTheory.Dists.LKJ(k, d.η))).L
end;

julia> rand(LKJL(4, 2)) # works!
4×4 LowerTriangular{Float64, Matrix{Float64}}:
1.0 ⋅ ⋅ ⋅
0.476038 0.879425 ⋅ ⋅
0.140615 -0.272203 0.95191 ⋅
0.363825 0.384274 -0.341334 0.776824

julia> pred = rand(m(4)) # also works!
(y = [0.44037889402215297, 1.5938652701305567, -0.23702427205378396, -0.5424312905721139], σ = [1.1175273174818694, 1.7836751630330654, 1.3148335815282792, 0.3861023795641398], ŷ = [0.3940654399522521, 0.8021362778335628, 0.3654790805746502, -1.595705932814744], L = [1.0 0.0 0.0 0.0; 0.4261101513982992 0.9046712877478309 0.0 0.0; 0.2419199606694756 -0.6653662683891962 0.7062311671963476 0.0; -0.07132419227849153 -0.057620246988085724 0.37930698942361735 0.920716554921896])

julia> post = sample(DynamicHMCChain, m(4) | (y=pred.y,), 1_000)
ERROR: StackOverflowError:
Stacktrace:
[1] xform(μ::Pushforward{TransformVariables.CorrCholeskyFactor, ProductMeasure{MappedArrays.ReadonlyMappedArray{Lebesgue{ℝ}, 1, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, MeasureTheory.var"#24#25"{Lebesgue{ℝ}}}}}, _data::NamedTuple{(), Tuple{}})
@ Soss ~/.julia/packages/Soss/9Knnt/src/primitives/xform.jl:134
[2] xform(μ::Pushforward{TransformVariables.CorrCholeskyFactor, ProductMeasure{MappedArrays.ReadonlyMappedArray{Lebesgue{ℝ}, 1, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, MeasureTheory.var"#24#25"{Lebesgue{ℝ}}}}}) (repeats 79983 times)
@ Soss ~/.julia/packages/Soss/9Knnt/src/primitives/xform.jl:135
```

The issue is `xform` calls `representative` on its argument and then calls `xform` on that, but after calling `representative` on `LKJL`, further calls to `representative` are idempotent, so we overflow.
MeasureTheory defines `TransformVariables.as` for `LKJL`, so we just overload `xform` to point to that:

```julia
julia> using Soss: TransformVariables

julia> Soss.xform(d::LKJL, _data::NamedTuple=NamedTuple()) = TransformVariables.as(d);

julia> xform(LKJL(4, 2))
TransformVariables.CorrCholeskyFactor(4)

julia> post = sample(DynamicHMCChain, m(4) | (y=pred.y,), 1_000)
4000-element MultiChain with 4 chains and schema (σ = Vector{Float64}, ŷ = Vector{Float64}, L = UpperTriangular{Float64, Matrix{Float64}})
(σ = [0.85±0.66, 0.81±0.71, 0.84±0.51, 0.87±0.64], ŷ = [-0.01±0.85, 0.1±1.3, -0.21±1.1, -0.02±1.1], L = [1.0±0.0 0.007±0.33 0.049±0.35 -0.0±0.28; 0.0±0.0 0.942±0.078 0.039±0.3 0.02±0.34; 0.0±0.0 0.0±0.0 0.877±0.11 0.02±0.43; 0.0±0.0 0.0±0.0 0.0±0.0 0.77±0.17])
```

But while we expected `L` to be `LowerTriangular`, it's actually `UpperTriangular`:

```julia
julia> post.L[1]
4×4 UpperTriangular{Float64, Matrix{Float64}}:
1.0 0.179572 -0.623481 -0.0554051
⋅ 0.983745 -0.121416 0.15332
⋅ ⋅ 0.772353 0.225203
⋅ ⋅ ⋅ 0.960576
```

This happens because even though `LKJL` is adapted from AltDistributions.jl, and this is in the same ecosystem as TransformVariables.jl, `CorrCholeskyFactor` transforms to an `UpperTriangular` matrix, while LKJL works with a `LowerTriangular` matrix.