diff --git a/example/Manifest.toml b/example/Manifest.toml
deleted file mode 100644
index 5d370aa..0000000
--- a/example/Manifest.toml
+++ /dev/null
@@ -1,1713 +0,0 @@
-# This file is machine-generated - editing it directly is not advised
-
-julia_version = "1.9.1"
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-project_hash = "f2ecc355a33269e2baf01e4a296e89edd7540dbc"
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diff --git a/example/targets/banana.jl b/example/targets/banana.jl
index b72b555..ee89cb5 100644
--- a/example/targets/banana.jl
+++ b/example/targets/banana.jl
@@ -8,22 +8,25 @@ using IrrationalConstants
 Multidimensional banana-shape distribution.
 
 # Fields 
-- 'dim::Int': Dimension of the distribution, must be >= 2
-- 'b::T': Banananicity constant, the larger "|b|" the more curved the banana
-- 'var::T': Variance of the first dimension, must be > 0
-
+$(FIELDS)
 
 # Explanation
 
 The banana distribution is obtained by applying a transformation ϕ to a multivariate normal 
-distribution "N(0, diag(var, 1, 1, …, 1))". The transformation ϕ is defined as
-"ϕ(x₁, … , xₚ) = (x₁, x₂ - B x₁² + var*B, x₃, … , xₚ)", 
+distribution ``\\mathcal{N}(0, \\text{diag}(var, 1, 1, …, 1))``. The transformation ϕ is defined as
+```math
+\phi(x_1, … , x_p) = (x_1, x_2 - B x_1^² + \text{var}*B, x_3, … , x_p)
+````
 which has a unit Jacobian determinant.
 
 Hence the density "fb" of a p-dimensional banana distribution is given by
-"fb(x₁, … , xₚ) = exp[ -½x₁²/var - ½(x₂ + B x₁² - var*B)² - ½(x₃² + x₄² + … + xₚ²)] / Z",
+```math
+fb(x_1, \dots, x_p) = \exp\left[ -\frac{1}{2}\frac{x_1^2}{\text{var}} -
+\frac{1}{2}(x_2 + B x_1^2 - \text{var}*B)^2 - \frac{1}{2}(x_3^2 + x_4^2 + \dots
++ x_p^2) \right] / Z,
+```
 where "B" is the "banananicity" constant, determining the curvature of a banana, and   
-"Z = sqrt(var * (2π)^p))" is the normalization constant.
+``Z = \\sqrt{\\text{var} * (2\\pi)^p)}`` is the normalization constant.
 
 
 # Reference
@@ -33,8 +36,11 @@ Gareth O. Roberts and Jeffrey S. Rosenthal
 Journal of computational and graphical statistics, Volume 18, Number 2 (2009): 349-367.
 """
 struct Banana{T<:Real} <: ContinuousMultivariateDistribution
+    "Dimension of the distribution, must be >= 2"
     dim::Int      # Dimension
+    "Banananicity constant, the larger |b| the more curved the banana"
     b::T          # Curvature
+    "Variance of the first dimension, must be > 0"
     var::T        # Variance
     function Banana{T}(dim::Int, b::T, var::T) where {T<:Real}
         dim >= 2 || error("dim must be >= 2")
diff --git a/example/targets/cross.jl b/example/targets/cross.jl
new file mode 100644
index 0000000..7c843f8
--- /dev/null
+++ b/example/targets/cross.jl
@@ -0,0 +1,39 @@
+using Distributions, Random
+"""
+    Cross(μ::Real=2.0, σ::Real=0.15)
+
+2-dimensional Cross distribution
+
+
+# Explanation
+
+The Cross distribution is a 2-dimension 4-component Gaussian distribution with a "cross" 
+shape that is symmetric about the y- and x-axises. The mixture is defined as
+
+```math
+\begin{aligned}
+p(x) =
+& 0.25 \mathcal{N}(x | (0, \mu), (\sigma, 1)) + \\
+& 0.25 \mathcal{N}(x | (\mu, 0), (1, \sigma)) + \\
+& 0.25 \mathcal{N}(x | (0, -\mu), (\sigma, 1)) + \\
+& 0.25 \mathcal{N}(x | (-\mu, 0), (1, \sigma)))
+\end{aligned}
+```
+
+where ``μ`` and ``σ`` are the mean and standard deviation of the Gaussian components, 
+respectively. See an example of the Cross distribution in Page 18 of [1].
+
+# Reference
+[1] Zuheng Xu, Naitong Chen, Trevor Campbell
+"MixFlows: principled variational inference via mixed flows."
+International Conference on Machine Learning, 2023
+"""
+Cross() = Cross(2.0, 0.15)
+function Cross(μ::T, σ::T) where {T<:Real}
+    return MixtureModel([
+        MvNormal([zero(μ), μ], [σ, one(σ)]),
+        MvNormal([-μ, one(μ)], [one(σ), σ]),
+        MvNormal([μ, one(μ)], [one(σ), σ]),
+        MvNormal([zero(μ), -μ], [σ, one(σ)]),
+    ])
+end
diff --git a/example/targets/neal_funnel.jl b/example/targets/neal_funnel.jl
new file mode 100644
index 0000000..66f58b0
--- /dev/null
+++ b/example/targets/neal_funnel.jl
@@ -0,0 +1,63 @@
+using Distributions, Random
+
+"""
+    Funnel{T<:Real}
+
+Multidimensional Neal's Funnel distribution
+
+# Fields 
+$(FIELDS)
+
+# Explanation
+
+The Neal's Funnel distribution is a p-dimensional distribution with a funnel shape, 
+originally proposed by Radford Neal in [2]. 
+The marginal distribution of ``x_1`` is Gaussian with mean "μ" and standard
+deviation "σ". The conditional distribution of ``x_2, \dots, x_p | x_1`` are independent 
+Gaussian distributions with mean 0 and standard deviation ``\\exp(x_1/2)``. 
+The generative process is given by
+```math
+x_1 \sim \mathcal{N}(\mu, \sigma^2), \quad x_2, \ldots, x_p \sim \mathcal{N}(0, \exp(x_1))
+```
+
+
+# Reference
+[1] Stan User’s Guide: 
+https://mc-stan.org/docs/2_18/stan-users-guide/reparameterization-section.html#ref-Neal:2003
+[2] Radford Neal 2003. “Slice Sampling.” Annals of Statistics 31 (3): 705–67.
+"""
+struct Funnel{T<:Real} <: ContinuousMultivariateDistribution
+    "Dimension of the distribution, must be >= 2"
+    dim::Int
+    "Mean of the first dimension"
+    μ::T
+    "Standard deviation of the first dimension, must be > 0"
+    σ::T
+    function Funnel{T}(dim::Int, μ::T, σ::T) where {T<:Real}
+        dim >= 2 || error("dim must be >= 2")
+        σ > 0 || error("σ must be > 0")
+        return new{T}(dim, μ, σ)
+    end
+end
+Funnel(dim::Int, μ::T, σ::T) where {T<:Real} = Funnel{T}(dim, μ, σ)
+Funnel(dim::Int, σ::T) where {T<:Real} = Funnel{T}(dim, zero(T), σ)
+Funnel(dim::Int) = Funnel(dim, 0.0, 9.0)
+
+Base.length(p::Funnel) = p.dim
+Base.eltype(p::Funnel{T}) where {T<:Real} = T
+
+function Distributions._rand!(rng::AbstractRNG, p::Funnel, x::AbstractVecOrMat)
+    T = eltype(x)
+    d, μ, σ = p.dim, p.μ, p.σ
+    d == size(x, 1) || error("Dimension mismatch")
+    x[1, :] .= randn(rng, T, size(x, 2)) .* σ .+ μ
+    x[2:end, :] .= randn(rng, T, d - 1, size(x, 2)) .* exp.(@view(x[1, :]) ./ 2)'
+    return x
+end
+
+function Distributions._logpdf(p::Funnel, x::AbstractVector)
+    d, μ, σ = p.dim, p.μ, p.σ
+    lpdf1 = logpdf(Normal(μ, σ), x[1])
+    lpdfs = logpdf.(Normal.(zeros(T, d - 1), exp(x[1] / 2)), @view(x[2:end]))
+    return lpdf1 + sum(lpdfs)
+end
diff --git a/example/targets/warped_gaussian.jl b/example/targets/warped_gaussian.jl
new file mode 100644
index 0000000..2179046
--- /dev/null
+++ b/example/targets/warped_gaussian.jl
@@ -0,0 +1,90 @@
+using Distributions, Random, LinearAlgebra, IrrationalConstants
+
+"""
+    WarpedGauss{T<:Real}
+
+2-dimensional warped Gaussian distribution
+
+# Fields 
+$(FIELDS)
+
+# Explanation
+
+The banana distribution is obtained by applying a transformation ϕ to a 2-dimensional normal 
+distribution ``\\mathcal{N}(0, diag(\\sigma_1, \\sigma_2))``. The transformation ϕ(x) is defined as
+```math
+ϕ(x_1, x_2) = (r*\cos(\theta + r/2), r*\sin(\theta + r/2)), 
+```
+where ``r = \\sqrt{x\_1^2 + x_2^2}``, ``\\theta = \\atan(x₂, x₁)``, 
+and "atan(y, x) ∈ [-π, π]" is the angle, in radians, between the positive x axis and the 
+ray to the point "(x, y)". See page 18. of [1] for reference.
+
+
+# Reference
+[1] Zuheng Xu, Naitong Chen, Trevor Campbell
+"MixFlows: principled variational inference via mixed flows."
+International Conference on Machine Learning, 2023
+"""
+struct WarpedGauss{T<:Real} <: ContinuousMultivariateDistribution
+    "Standard deviation of the first dimension, must be > 0"
+    σ1::T
+    "Standard deviation of the second dimension, must be > 0"
+    σ2::T
+    function WarpedGauss{T}(σ1, σ2) where {T<:Real}
+        σ1 > 0 || error("σ₁ must be > 0")
+        σ2 > 0 || error("σ₂ must be > 0")
+        return new{T}(σ1, σ2)
+    end
+end
+WarpedGauss(σ1::T, σ2::T) where {T<:Real} = WarpedGauss{T}(σ1, σ2)
+WarpedGauss() = WarpedGauss(1.0, 0.12)
+
+Base.length(p::WarpedGauss) = 2
+Base.eltype(p::WarpedGauss{T}) where {T<:Real} = T
+Distributions.sampler(p::WarpedGauss) = p
+
+# Define the transformation function φ and the inverse ϕ⁻¹ for the warped Gaussian distribution
+function ϕ!(p::WarpedGauss, z::AbstractVector)
+    length(z) == 2 || error("Dimension mismatch")
+    x, y = z
+    r = norm(z)
+    θ = atan(y, x) #in [-π , π]
+    θ -= r / 2
+    z .= r .* [cos(θ), sin(θ)]
+    return z
+end
+
+function ϕ⁻¹(p::WarpedGauss, z::AbstractVector)
+    length(z) == 2 || error("Dimension mismatch")
+    x, y = z
+    r = norm(z)
+    θ = atan(y, x) #in [-π , π]
+    # increase θ depending on r to "smear"
+    θ += r / 2
+
+    # get the x,y coordinates foαtransformed point
+    xn = r * cos(θ)
+    yn = r * sin(θ)
+    # compute jacobian
+    logJ = log(r)
+    return [xn, yn], logJ
+end
+
+function Distributions._rand!(rng::AbstractRNG, p::WarpedGauss, x::AbstractVecOrMat)
+    size(x, 1) == 2 || error("Dimension mismatch")
+    σ₁, σ₂ = p.σ₁, p.σ₂
+    randn!(rng, x)
+    x .*= [σ₁, σ₂]
+    for y in eachcol(x)
+        ϕ!(p, y)
+    end
+    return x
+end
+
+function Distributions._logpdf(p::WarpedGauss, x::AbstractVector)
+    size(x, 1) == 2 || error("Dimension mismatch")
+    σ₁, σ₂ = p.σ₁, p.σ₂
+    S = [σ₁, σ₂] .^ 2
+    z, logJ = ϕ⁻¹(p, x)
+    return -sum(z .^ 2 ./ S) / 2 - IrrationalConstants.log2π - log(σ₁) - log(σ₂) + logJ
+end