More fixes.

src/constructors.jl

@@ -28,20 +28,18 @@ struct HMCSampler{T<:Real} <: AbstractHMCSampler{T}
     metric::AbstractMetric
     "[`AbstractAdaptor`](@ref)."
     adaptor::AbstractAdaptor
-    "Adaptation steps if any"
-    n_adapts::Int
 end
 
-function HMCSampler(κ, metric, adaptor; n_adapts = 0)
+function HMCSampler(κ, metric, adaptor)
     T = collect(typeof(metric).parameters)[1]
-    return HMCSampler{T}(κ, metric, adaptor, n_adapts)
+    return HMCSampler{T}(κ, metric, adaptor)
 end
 
 ############
 ### NUTS ###
 ############
 """
-    NUTS(n_adapts::Int, δ::Real; max_depth::Int=10, Δ_max::Real=1000, init_ϵ::Real=0)
+    NUTS(δ::Real; max_depth::Int=10, Δ_max::Real=1000, init_ϵ::Real=0, init_ϵ = 0.0, integrator = :leapfrog, metric = :diagonal)
 
 No-U-Turn Sampler (NUTS) sampler.
 
@@ -52,7 +50,7 @@ $(FIELDS)
 # Usage:
 
 ```julia
-NUTS(n_adapts=1000, δ=0.65)  # Use 1000 adaption steps, and target accept ratio 0.65.
+NUTS(δ=0.65)  # Use target accept ratio 0.65.
 ```
 """
 struct NUTS{T<:Real} <: AbstractHMCSampler{T}
@@ -97,7 +95,7 @@ $(FIELDS)
 # Usage:
 
 ```julia
-HMC(init_ϵ=0.05, n_leapfrog=10)
+HMC(init_ϵ=0.05, n_leapfrog=10, integrator = :leapfrog, metric = :diagonal)
 ```
 """
 struct HMC{T<:Real} <: AbstractHMCSampler{T}
@@ -119,7 +117,7 @@ end
 ### HMCDA ###
 #############
 """
-    HMCDA(n_adapts::Int, δ::Real, λ::Real; ϵ::Real=0)
+    HMCDA(δ::Real, λ::Real; ϵ::Real=0, integrator = :leapfrog, metric = :diagonal)
 
 Hamiltonian Monte Carlo sampler with Dual Averaging algorithm.
 
@@ -130,7 +128,7 @@ $(FIELDS)
 # Usage:
 
 ```julia
-HMCDA(n_adapts=200, δ=0.65, λ=0.3)
+HMCDA(δ=0.65, λ=0.3)
 ```
 
 For more information, please view the following paper ([arXiv link](https://arxiv.org/abs/1111.4246)):