Code Quality Checking

.github/workflows/CI.yml

@@ -16,6 +16,7 @@ jobs:
       matrix:
         version:
           - '1.11' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
+          - '1'
         os:
           - ubuntu-latest
         arch:

Project.toml

@@ -8,10 +8,7 @@ ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 FunctionZeros = "b21f74c0-b399-568f-9643-d20f4fa2c814"
 HCubature = "19dc6840-f33b-545b-b366-655c7e3ffd49"
-Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
-KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
@@ -20,14 +17,18 @@ StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [weakdeps]
+DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
+Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
+KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [extensions]
-PlotsExt = "Plots"
+PlotsExt = ["Plots", "Interpolations", "KernelDensity"]
 
 [compat]
 ArgCheck = "2.5.0"
 Distributions = "v0.24.6, 0.25"
+DynamicPPL = "0.25 - 0.39"
 FunctionZeros = "0.2.0,0.3.0, 1"
 HCubature = "1"
 Interpolations = "0.14.0,0.15.0,0.16.0"

src/SequentialSamplingModels.jl

@@ -64,6 +64,7 @@ export PoissonRace
 export ShiftedLogNormal
 export SSM1D
 export SSM2D
+export SSMProductDistribution
 export stDDM
 export ContinuousMultivariateSSM
 export Wald
@@ -85,7 +86,7 @@ export plot_model
 export plot_model!
 export plot_quantiles
 export plot_quantiles!
-export predict_distribution
+export product_distribution
 export rand
 export simulate
 export std

src/multi_choice_models/MDFT.jl

@@ -272,7 +272,7 @@ make_default_contrast(3)
  -0.5  -0.5   1.0
 ```
 """
-function make_default_contrast(n)
+function make_default_contrast(n::Integer)
     C = fill(0.0, n, n)
     C .= -1 / (n - 1)
     for r ∈ 1:n

src/product_distribution.jl

@@ -1,17 +1,48 @@
+"""
+    SSMProductDistribution
+
+Wrapper around `ProductDistribution` for sequential sampling models.
+This type allows us to define `logpdf` methods for `NamedTuple` data
+without type piracy.
+"""
+struct SSMProductDistribution{D <: ProductDistribution}
+    dist::D
+end
+
+"""
+    product_distribution(dists)
+
+Create a product distribution from a vector of distributions.
+Returns an `SSMProductDistribution` for SSM types, or a standard
+`ProductDistribution` for other types.
+"""
+function product_distribution(dists::AbstractVector)
+    pd = ProductDistribution(dists)
+    # Check if this is an SSM that produces NamedTuple data
+    if eltype(dists) <: SSM2D
+        return SSMProductDistribution(pd)
+    else
+        return pd
+    end
+end
+
+Base.size(s::SSMProductDistribution, dims...) = size(s.dist, dims...)
+Base.length(s::SSMProductDistribution) = length(s.dist)
+
 function rand(
     rng::AbstractRNG,
-    s::Sampleable{T, R}
-) where {T <: Matrixvariate, R <: SequentialSamplingModels.Mixed}
+    s::SSMProductDistribution
+)
     n = size(s, 2)
     data = (; choice = fill(0, n), rt = fill(0.0, n))
     return rand!(rng, s, data)
 end
 
 function rand(
     rng::AbstractRNG,
-    s::Sampleable{T, R},
+    s::SSMProductDistribution,
     dims::Dims
-) where {T <: Matrixvariate, R <: SequentialSamplingModels.Mixed}
+)
     n = size(s, 2)
     ax = map(Base.OneTo, dims)
     data = [(; choice = fill(0, n), rt = fill(0.0, n)) for _ in Iterators.product(ax...)]
@@ -20,23 +51,23 @@ end
 
 function rand!(
     rng::AbstractRNG,
-    s::Sampleable{T, R},
+    s::SSMProductDistribution,
     data::NamedTuple
-) where {T <: Matrixvariate, R <: SequentialSamplingModels.Mixed}
+)
     for i ∈ 1:size(s, 2)
-        data.choice[i], data.rt[i] = rand(rng, s.dists[i])
+        data.choice[i], data.rt[i] = rand(rng, s.dist.dists[i])
     end
     return data
 end
 
-function logpdf(d::ProductDistribution, data_array::Array{<:NamedTuple, N}) where {N}
+function logpdf(d::SSMProductDistribution, data_array::Array{<:NamedTuple, N}) where {N}
     return [logpdf(d, data) for data ∈ data_array]
 end
 
-function logpdf(d::ProductDistribution, data::NamedTuple)
+function logpdf(d::SSMProductDistribution, data::NamedTuple)
     LL = 0.0
-    for i ∈ 1:length(d.dists)
-        LL += logpdf(d.dists[i], data.choice[i], data.rt[i])
+    for i ∈ 1:length(d.dist.dists)
+        LL += logpdf(d.dist.dists[i], data.choice[i], data.rt[i])
     end
     return LL
 end

test/Project.toml

@@ -1,6 +1,8 @@
 [deps]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
@@ -14,5 +16,5 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 TuringUtilities = "35dc62cd-6c01-44e1-a736-6cc36bfce0cc"
 
-[sources.TuringUtilities]
-url = "https://github.com/itsdfish/TuringUtilities.jl"
+[sources]
+TuringUtilities = {rev = "main", url = "https://github.com/itsdfish/TuringUtilities.jl"}

test/codequality.jl

@@ -0,0 +1,21 @@
+@safetestset "Code Quality" begin
+
+    # check code quality via Aqua
+    @safetestset "Aqua" begin
+        using Aqua
+        using SequentialSamplingModels
+        Aqua.test_all(
+            SequentialSamplingModels;
+            ambiguities = false,
+            deps_compat = (check_extras = false,),
+            project_extras = false
+        )
+    end
+
+    # test JET
+    @safetestset "JET" begin
+        using JET
+        using SequentialSamplingModels
+        JET.test_package(SequentialSamplingModels; target_modules = (SequentialSamplingModels,))
+    end
+end

test/product_distribution_tests.jl

@@ -2,6 +2,7 @@
     @safetestset "rand SSM1D 1" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         walds = [Wald(; ν = 2.5, α = 0.1, τ = 0.2), Wald(; ν = 1.5, α = 1, τ = 10)]
@@ -15,6 +16,7 @@
     @safetestset "rand SSM1D 2" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         walds = [Wald(; ν = 2.5, α = 0.1, τ = 0.2), Wald(; ν = 1.5, α = 1, τ = 10)]
@@ -28,6 +30,7 @@
     @safetestset "rand logpdf 1" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         walds = [Wald(; ν = 2.5, α = 0.1, τ = 0.2), Wald(; ν = 1.5, α = 1, τ = 10)]
@@ -41,6 +44,7 @@
     @safetestset "logpdf SSM1D 2" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         walds = [Wald(; ν = 2.5, α = 0.1, τ = 0.2), Wald(; ν = 1.5, α = 1, τ = 10)]
@@ -54,6 +58,7 @@
     @safetestset "rand SSM2D 1" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         lbas = [
@@ -70,6 +75,7 @@
     @safetestset "rand SSM2D 2" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         lbas = [
@@ -86,6 +92,7 @@
     @safetestset "logpdf SSM2D 1" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         lbas = [
@@ -103,6 +110,7 @@
     @safetestset "logpdf SSM2D 2" begin
         using Distributions
         using SequentialSamplingModels
+        using SequentialSamplingModels: product_distribution
         using Test
 
         lbas = [