diff --git a/Project.toml b/Project.toml
index 26a3a666..d26cc4d2 100644
--- a/Project.toml
+++ b/Project.toml
@@ -19,7 +19,6 @@ Infinities = "e1ba4f0e-776d-440f-acd9-e1d2e9742647"
InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
KeywordCalls = "4d827475-d3e4-43d6-abe3-9688362ede9f"
-LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
@@ -55,7 +54,6 @@ IfElse = "0.1"
Infinities = "0.1"
InverseFunctions = "0.1"
KeywordCalls = "0.2"
-LazyArrays = "0.22"
LogExpFunctions = "0.3.3"
MLStyle = "0.4"
MacroTools = "0.5"
diff --git a/src/MeasureTheory.jl b/src/MeasureTheory.jl
index e43642d7..86108fbc 100644
--- a/src/MeasureTheory.jl
+++ b/src/MeasureTheory.jl
@@ -130,7 +130,6 @@ include("combinators/affine.jl")
include("combinators/weighted.jl")
include("combinators/product.jl")
include("combinators/transforms.jl")
-include("combinators/exponential-families.jl")
include("resettable-rng.jl")
include("realized.jl")
include("combinators/chain.jl")
diff --git a/src/combinators/exponential-families.jl b/src/combinators/exponential-families.jl
deleted file mode 100644
index e3e169a7..00000000
--- a/src/combinators/exponential-families.jl
+++ /dev/null
@@ -1,119 +0,0 @@
-export ExponentialFamily
-using LazyArrays
-
-@concrete terse struct ExponentialFamily <: AbstractTransitionKernel
- support_contains
- base
- mdim
- pdim
- t
- x
- a
-end
-
-MeasureBase.insupport(fam::ExponentialFamily, x) = fam.support_contains(x)
-
-function ExponentialFamily(support_contains, base, mdim, pdim, t, a)
- return ExponentialFamily(support_contains, base, mdim, pdim, t, I, a)
-end
-
-function MeasureBase.powermeasure(fam::ExponentialFamily, dims::NTuple)
- support_contains(x) = all(xj -> fam.support_contains(xj), x)
- t = Tuple((y -> f.(y) for f in fam.t))
- a(η) = LazyArrays.BroadcastArray(fam.a, η)
- p = prod(dims)
- ExponentialFamily(
- support_contains,
- fam.base^dims,
- fam.mdim * p,
- fam.pdim * p,
- t,
- fam.x,
- a,
- )
-end
-
-powermeasure(fam::ExponentialFamily, ::Tuple{}) = fam
-
-@concrete terse struct ExpFamMeasure <: AbstractMeasure
- fam
- η # instantiated to a value
- a # instantiated to a value
-end
-
-MeasureBase.insupport(μ::ExpFamMeasure, x) = μ.fam.support_contains(x)
-
-@inline function (fam::ExponentialFamily)(β)
- η = fam.x * β
- a = fam.a(η)
- ExpFamMeasure(fam, η, a)
-end
-
-MeasureBase.basemeasure(d::ExpFamMeasure) = d.fam.base
-
-tracedot(a::AbstractVector, b::AbstractVector) = dot(a, b)
-
-tracedot(a::AbstractVector, x, b::AbstractVector) = dot(a, x, b)
-
-tracedot(a, b) = sum((dot(view(a, :, j), view(b, :, j)) for j in 1:size(a, 2)))
-
-tracedot(a, x, b) =
- sum(1:size(a, 2)) do j
- dot(view(a, :, j), x, view(b, :, j))
- end
-
-# @inline function tracedot(a::BlockDiag, b::BlockDiag)
-# numblocks = length(a.blocks)
-# sum(tracedot(a.blocks[j], b.blocks[j]) for j in 1:length(a.blocks))
-# end
-
-# @inline function tracedot(a::BlockDiag, x::BlockDiag, b::BlockDiag)
-# numblocks = length(x.blocks)
-# sum(tracedot(a.blocks[j], x.blocks[j], b.blocks[j]) for j in 1:length(x.blocks))
-# end
-
-function logdensity_def(d::ExpFamMeasure, y)
- t = ApplyArray(vcat, (f.(y) for f in d.fam.t)...)
- η = d.η
- dot(t, η)
-end
-
-function withX(fam::ExponentialFamily, x)
- @inline t(y) = fam.t.(y)
- newx = ApplyArray(kron, x, fam.x)
- η(β) = fam.η.(β)
- a(β) = sum(fam.a, β)
- ExponentialFamily(fam.base^size(x, 1), t, x, η, a)
-end
-
-@concrete terse struct ExpFamLikelihood <: AbstractLikelihood
- fam
- y
- tᵀx
- c
-end
-
-# function regression(fam, uᵀ, vᵀ)
-
-# end
-
-function MeasureBase.likelihoodof(fam::ExponentialFamily, y)
- c = logdensityof(fam.base, y)
- t = ApplyArray(vcat, (f.(y) for f in fam.t)...)
- tᵀx = t' * fam.x
- ExpFamLikelihood(fam, y, tᵀx, c)
-end
-
-@inline function logdensityof(ℓ::ExpFamLikelihood, β)
- xβ = ApplyArray(*, ℓ.fam.x, β)
- a = sum(ℓ.fam.a(xβ))
- # a = sum(ℓ.fam.a, ApplyArray(*, ℓ.fam.uᵀ', ℓ.fam.vᵀ, β))
- ℓ.c + dot(ℓ.tᵀx, β) - a
-end
-
-basemeasure(fam::ExponentialFamily) = fam.base
-
-# function stack_functions(funs, inds)
-# function(x::AbstractArray{T,N}) where {T,N}
-# ApplyArray(cat, )
-# end
diff --git a/src/combinators/tweedie.jl b/src/combinators/tweedie.jl
deleted file mode 100644
index b18a3fb8..00000000
--- a/src/combinators/tweedie.jl
+++ /dev/null
@@ -1,159 +0,0 @@
-export Tweedie
-
-abstract type AbstractEDM <: AbstractTransitionKernel end
-
-"""
-From https://en.wikipedia.org/wiki/Tweedie_distribution:
-
-> The Tweedie distributions include a number of familiar distributions as well as
-some unusual ones, each being specified by the domain of the index parameter. We
-have the
-
- extreme stable distribution, p < 0,
- normal distribution, p = 0,
- Poisson distribution, p = 1,
- compound Poisson-gamma distribution, 1 < p < 2,
- gamma distribution, p = 2,
- positive stable distributions, 2 < p < 3,
- Inverse Gaussian distribution, p = 3,
- positive stable distributions, p > 3, and
- extreme stable distributions, p = ∞.
-
-For 0 < p < 1 no Tweedie model exists. Note that all stable distributions mean
-actually generated by stable distributions.
-"""
-struct Tweedie{S,B,D,P} <: AbstractEDM
- support_contains::S
- base::B
- dim::D
- p::P
-end
-
-struct TweedieMeasure{B,Θ,P,S,C} <: AbstractMeasure
- fam::Tweedie{B,Θ,P}
- θ::Θ
- σ::S
- cumulant::C
-end
-
-mean(d::TweedieMeasure) = tweedie_mean(d.fam.p, d.θ)
-
-var(d::TweedieMeasure) = d.σ^2 * mean(d)^d.fam.p
-
-###############################################################################
-# Tweedie cumulants
-
-@inline function tweedie_cumulant(p::P, θ) where {P}
- if p == zero(P)
- return 0.5 * θ^2
- elseif p == one(P)
- return exp(θ)
- elseif p == 2
- return -log(-θ)
- else
- α = (p - 2) / (p - 1)
- coeff = (α - 1) / α
- return coeff * (θ / (α - 1))^α
- end
-end
-
-@inline function tweedie_cumulant(::StaticFloat64{0.0}, θ)
- return 0.5 * θ^2
-end
-
-@inline function tweedie_cumulant(::StaticFloat64{1.0}, θ)
- return exp(θ)
-end
-
-@inline function tweedie_cumulant(::StaticFloat64{2.0}, θ)
- return -log(-θ)
-end
-
-@generated function tweedie_cumulant(::StaticFloat64{p}, θ) where {p}
- α = (p - 2) / (p - 1)
- coeff = (α - 1) / α
-
- quote
- $(Expr(:meta, :inline))
- coeff * (θ / (α - 1))^α
- end
-end
-
-@inline function (fam::Tweedie)(par)
- base = fam.base(par.σ)
- θ = fam.θ(par)
- η = fam.η(θ)
- t = fam.t
- a = fam.a(θ)
- TweedieMeasure(base, θ, p, σ)
-end
-
-###############################################################################
-# Tweedie mean function
-
-@inline function tweedie_mean(p::P, θ) where {P}
- if p == zero(P)
- return θ
- elseif p == one(P)
- return exp(θ)
- elseif p == 2
- return inv(log(-θ))
- else
- α_minus_1 = (p - 2) / (p - 1) - 1
- return (θ / α_minus_1)^α_minus_1
- end
-end
-
-@inline function tweedie_mean(::StaticFloat64{0.0}, θ)
- return θ
-end
-
-@inline function tweedie_mean(::StaticFloat64{1.0}, θ)
- return exp(θ)
-end
-
-@inline function tweedie_mean(::StaticFloat64{2.0}, θ)
- return inv(log(-θ))
-end
-
-@generated function tweedie_mean(::StaticFloat64{p}, θ) where {p}
- α_minus_1 = (p - 2) / (p - 1) - 1
-
- quote
- $(Expr(:meta, :inline))
- (θ / α_minus_1)^α_minus_1
- end
-end
-
-basemeasure(d::TweedieMeasure) = d.base
-
-function logdensity_def(d::TweedieMeasure, x)
- mydot(x, d.θ) - d.cumulant
-end
-
-function MeasureBase.powermeasure(fam::Tweedie, dims::NTuple{N,I}) where {N,I}
- base(σ) = fam.base(σ)^dims
- a = AffineTransform((σ = prod(dims),)) ∘ fam.a
- Tweedie(fam.base^dims, fam.θ, fam.η, t, a)
-end
-
-struct TweedieLikelihood{C,Θ,H,T,A} <: AbstractLikelihood
- c::C
- θ::Θ
- η::H
- t::T
- a::A
-end
-
-function likelihoodof(fam::Tweedie, x)
- c = logdensityof(fam.base, x)
- t = fam.t(x)
- TweedieLikelihood(c, fam.θ, fam.η, t, fam.a)
-end
-
-@inline function logdensity_def(ℓ::TweedieLikelihood, par)
- θ = ℓ.θ(par)
- mydot(θ, ℓ.t) - ℓ.a(θ) + ℓ.c
-end
-
-basemeasure(fam::Tweedie) = fam.base