Make AdvancedMH compatible with AbstractMCMC 5

Project.toml

@@ -1,6 +1,6 @@
 name = "AdvancedMH"
 uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170"
-version = "0.7.6"
+version = "0.7.7"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -23,18 +23,18 @@ AdvancedMHMCMCChainsExt = "MCMCChains"
 AdvancedMHStructArraysExt = "StructArrays"
 
 [compat]
-AbstractMCMC = "4, 5"
+AbstractMCMC = "5"
 DiffResults = "1"
-Distributions = "0.20 - 0.25"
-LinearAlgebra = "1.6 - 1.11"
-Random = "1.6 - 1.11"
+Distributions = "0.25"
 FillArrays = "1"
 ForwardDiff = "0.10"
 LogDensityProblems = "2"
-MCMCChains = "5, 6"
+MCMCChains = "6.0.4"
 Requires = "1"
 StructArrays = "0.6"
 julia = "1.6"
+LinearAlgebra = "1.6"
+Random = "1.6"
 
 [extras]
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"

README.md

@@ -138,21 +138,21 @@ AdvancedMH.jl implements the interface of [AbstractMCMC](https://github.com/Turi
 
 ```julia
 # Sample 4 chains from the posterior serially, without thread or process parallelism.
-chain = sample(model, RWMH(init_params), MCMCSerial(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
+chain = sample(model, spl, MCMCSerial(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
 
 # Sample 4 chains from the posterior using multiple threads.
-chain = sample(model, RWMH(init_params), MCMCThreads(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
+chain = sample(model, spl, MCMCThreads(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
 
 # Sample 4 chains from the posterior using multiple processes.
-chain = sample(model, RWMH(init_params), MCMCDistributed(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
+chain = sample(model, spl, MCMCDistributed(), 100000, 4; param_names=["μ","σ"], chain_type=Chains)
 ```
 
 ## Metropolis-adjusted Langevin algorithm (MALA)
 
 AdvancedMH.jl also offers an implementation of [MALA](https://en.wikipedia.org/wiki/Metropolis-adjusted_Langevin_algorithm) if the `ForwardDiff` and `DiffResults` packages are available. 
 
 A `MALA` sampler can be constructed by `MALA(proposal)` where `proposal` is a function that
-takes the gradient computed at the current sample. It is required to specify an initial sample `init_params` when calling `sample`.
+takes the gradient computed at the current sample. It is required to specify an initial sample `initial_params` when calling `sample`.
 
 ```julia
 # Import the package.
@@ -180,7 +180,7 @@ model = DensityModel(density)
 spl = MALA(x -> MvNormal((σ² / 2) .* x, σ² * I))
 
 # Sample from the posterior.
-chain = sample(model, spl, 100000; init_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
+chain = sample(model, spl, 100000; initial_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
 ```
 
 ### Usage with [`LogDensityProblems.jl`](https://github.com/tpapp/LogDensityProblems.jl)
@@ -192,5 +192,5 @@ Using our implementation of the `LogDensityProblems.jl` interface above:
 ```julia
 using LogDensityProblemsAD
 model_with_ad = LogDensityProblemsAD.ADgradient(Val(:ForwardDiff), LogTargetDensity())
-sample(model_with_ad, spl, 100000; init_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
+sample(model_with_ad, spl, 100000; initial_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
 ```

src/mh-core.jl

@@ -33,7 +33,7 @@ spl = MetropolisHastings(proposal)
 When using `MetropolisHastings` with the function `sample`, the following keyword
 arguments are allowed:
 
-- `init_params` defines the initial parameterization for your model. If
+- `initial_params` defines the initial parameterization for your model. If
 none is given, the initial parameters will be drawn from the sampler's proposals.
 - `param_names` is a vector of strings to be assigned to parameters. This is only
 used if `chain_type=Chains`.
@@ -77,10 +77,10 @@ function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::DensityModelOrLogDensityModel,
     sampler::MHSampler;
-    init_params=nothing,
+    initial_params=nothing,
     kwargs...
 )
-    params = init_params === nothing ? propose(rng, sampler, model) : init_params
+    params = initialparams === nothing ? propose(rng, sampler, model) : initial_params
     transition = AdvancedMH.transition(sampler, model, params)
     return transition, transition
 end

test/runtests.jl

@@ -182,7 +182,7 @@ include("util.jl")
         val = [0.4, 1.2]
 
         # Sample from the posterior.
-        chain1 = sample(model, spl1, 10, init_params = val)
+        chain1 = sample(model, spl1, 10, initial_params = val)
 
         @test chain1[1].params == val
     end
@@ -265,7 +265,7 @@ include("util.jl")
         spl1 = MALA(x -> MvNormal((σ² / 2) .* x, σ² * I))
 
         # Sample from the posterior with initial parameters.
-        chain1 = sample(model, spl1, 100000; init_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
+        chain1 = sample(model, spl1, 100000; initial_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])
 
         @test mean(chain1.μ) ≈ 0.0 atol=0.1
         @test mean(chain1.σ) ≈ 1.0 atol=0.1
@@ -276,7 +276,7 @@ include("util.jl")
                 admodel,
                 spl1,
                 100000;
-                init_params=ones(2),
+                initial_params=ones(2),
                 chain_type=StructArray,
                 param_names=["μ", "σ"]
             )