Fix deprecations (#62)

Project.toml

@@ -17,9 +17,10 @@ julia = "1"
 [extras]
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["StructArrays", "MCMCChains", "Test", "ForwardDiff", "DiffResults"]
+test = ["DiffResults", "ForwardDiff", "LinearAlgebra", "MCMCChains", "StructArrays", "Test"]

README.md

@@ -16,6 +16,8 @@ using AdvancedMH
 using Distributions
 using MCMCChains
 
+using LinearAlgebra
+
 # Generate a set of data from the posterior we want to estimate.
 data = rand(Normal(0, 1), 30)
 
@@ -28,7 +30,7 @@ density(θ) = insupport(θ) ? sum(logpdf.(dist(θ), data)) : -Inf
 model = DensityModel(density)
 
 # Set up our sampler with a joint multivariate Normal proposal.
-spl = RWMH(MvNormal(2,1))
+spl = RWMH(MvNormal(zeros(2), I))
 
 # Sample from the posterior.
 chain = sample(model, spl, 100000; param_names=["μ", "σ"], chain_type=Chains)
@@ -135,6 +137,8 @@ using DiffResults
 using ForwardDiff
 using StructArrays
 
+using LinearAlgebra
+
 # Generate a set of data from the posterior we want to estimate.
 data = rand(Normal(0, 1), 30)
 
@@ -147,8 +151,8 @@ density(θ) = insupport(θ) ? sum(logpdf.(dist(θ), data)) : -Inf
 model = DensityModel(density)
 
 # Set up the sampler with a multivariate Gaussian proposal.
-sigma = 1e-1
-spl = MALA(x -> MvNormal((sigma^2 / 2) .* x, sigma))
+σ² = 0.01
+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"])

test/emcee.jl

@@ -57,7 +57,7 @@
 
             # perform stretch move and sample from normal distribution in initial step
             Random.seed!(100)
-            sampler = Ensemble(1_000, StretchProposal(MvNormal(2, 1)))
+            sampler = Ensemble(1_000, StretchProposal(MvNormal(zeros(2), I)))
             chain = sample(model, sampler, 1_000;
                            param_names = ["logs", "m"], chain_type = Chains)
             @test chain isa Chains

test/runtests.jl

@@ -1,12 +1,13 @@
 using AdvancedMH
+using DiffResults
 using Distributions
-using StructArrays
+using ForwardDiff
 using MCMCChains
+using StructArrays
 
+using LinearAlgebra
 using Random
 using Test
-using DiffResults
-using ForwardDiff
 
 include("util.jl")
 
@@ -28,7 +29,7 @@ include("util.jl")
     @testset "StaticMH" begin
         # Set up our sampler with initial parameters.
         spl1 = StaticMH([Normal(0,1), Normal(0, 1)])
-        spl2 = StaticMH(MvNormal([0.0, 0.0], 1))
+        spl2 = StaticMH(MvNormal(zeros(2), I))
 
         # Sample from the posterior.
         chain1 = sample(model, spl1, 100000; chain_type=StructArray, param_names=["μ", "σ"])
@@ -44,7 +45,7 @@ include("util.jl")
     @testset "RandomWalk" begin
         # Set up our sampler with initial parameters.
         spl1 = RWMH([Normal(0,1), Normal(0, 1)])
-        spl2 = RWMH(MvNormal([0.0, 0.0], 1))
+        spl2 = RWMH(MvNormal(zeros(2), I))
 
         # Sample from the posterior.
         chain1 = sample(model, spl1, 100000; chain_type=StructArray, param_names=["μ", "σ"])
@@ -245,8 +246,8 @@ include("util.jl")
 
     @testset "MALA" begin
         # Set up the sampler.
-        sigma = 1e-1
-        spl1 = MALA(x -> MvNormal((sigma^2 / 2) .* x, sigma))
+        σ² = 0.01
+        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"])