Format .jl files

Author: itsdfish

src/multi_choice_models/maaDDM.jl

@@ -181,6 +181,6 @@ function increment!(rng, dist::maaDDM, location)
         return Δ * (ϕ * ω * (θ * ν[1, 1] - ν[2, 1]) + (1 - ω) * (θ * ν[1, 2] - ν[2, 2])) +
                noise(rng, σ)
     end
-    @argcheck location ∈ [1,2,3,4]
+    @argcheck location ∈ [1, 2, 3, 4]
     return -100.0
 end

test/multi_choice_models/multiattribute_attentional_diffusion_tests.jl

@@ -151,7 +151,7 @@
         @test Δ2s ≈ [1, 1, -2, -2]
     end
 
-        @safetestset "increment! invalid" begin
+    @safetestset "increment! invalid" begin
         using Test, SequentialSamplingModels
         using SequentialSamplingModels: increment!
 

test/multi_choice_models/poisson_race_tests.jl

@@ -107,7 +107,7 @@
         model = PoissonRace(; parms...)
         @test values(parms) == params(model)
     end
-    
+
     @safetestset "parameter checks" begin
         @safetestset "all valid" begin
             using Test

test/single_choice_models/shiftedlognormaldistribution.jl

@@ -71,45 +71,45 @@
             using Distributions
             using SequentialSamplingModels
 
-            dist = ShiftedLogNormal(; ν = -1, σ = .5, τ = .1)
-            @test mean(LogNormal(-1, .5)) + .1 ≈ mean(dist)
+            dist = ShiftedLogNormal(; ν = -1, σ = 0.5, τ = 0.1)
+            @test mean(LogNormal(-1, 0.5)) + 0.1 ≈ mean(dist)
         end
 
         @safetestset "2" begin
             using Test
             using Distributions
             using SequentialSamplingModels
             using StableRNGs
-            
+
             rng = StableRNG(32)
 
-            dist = ShiftedLogNormal(; ν = -1, σ = .5, τ = .1)
+            dist = ShiftedLogNormal(; ν = -1, σ = 0.5, τ = 0.1)
             data = rand(dist, 10_000)
-            @test mean(data) ≈ mean(dist) rtol = .01
+            @test mean(data) ≈ mean(dist) rtol = 0.01
         end
     end
 
-    @safetestset "cdf" begin 
+    @safetestset "cdf" begin
         using Test
         using Distributions
         using SequentialSamplingModels
         using StableRNGs
-        
+
         rng = StableRNG(345)
 
-        dist = ShiftedLogNormal(; ν = -1, σ = .5, τ = .1)
+        dist = ShiftedLogNormal(; ν = -1, σ = 0.5, τ = 0.1)
         data = rand(dist, 10_000)
 
-        @test cdf(dist, .5) ≈ mean(data .≤ .5) rtol = .02
+        @test cdf(dist, 0.5) ≈ mean(data .≤ 0.5) rtol = 0.02
     end
-        
+
     @safetestset "std" begin
         using Test
         using Distributions
         using SequentialSamplingModels
 
-        dist = ShiftedLogNormal(; ν = -1, σ = .5, τ = .1)
-        @test std(LogNormal(-1, .5)) ≈ std(dist)
+        dist = ShiftedLogNormal(; ν = -1, σ = 0.5, τ = 0.1)
+        @test std(LogNormal(-1, 0.5)) ≈ std(dist)
     end
 
     @safetestset "params" begin

test/single_choice_models/wald_tests.jl

@@ -24,8 +24,8 @@
             integral, _ = quadgk(t -> pdf(dist, t), 0.130, 20.0)
             @test integral ≈ 1 atol = 1e-4
 
-            dist = Wald(; ν = 2.0, η = 0.0, α = 0.3, τ = .130)
-            integral, _ = quadgk(t -> pdf(dist, t), .130, 20.0)
+            dist = Wald(; ν = 2.0, η = 0.0, α = 0.3, τ = 0.130)
+            integral, _ = quadgk(t -> pdf(dist, t), 0.130, 20.0)
             @test integral ≈ 1 atol = 1e-4
         end
     end
@@ -63,30 +63,30 @@
         using StableRNGs
 
         rng = StableRNG(34)
-        
+
         Θ = (; ν = 2.0, η = 0.3, α = 0.3, τ = 0.130)
         dist = Wald(; Θ...)
         data = rand(rng, dist, 10_000)
 
-        νs = range(Θ.ν * .8, Θ.ν * 1.2, 100)
+        νs = range(Θ.ν * 0.8, Θ.ν * 1.2, 100)
         LLs = map(ν -> loglikelihood(Wald(; Θ..., ν), data), νs)
         _, max_idx = findmax(LLs)
-        @test νs[max_idx] ≈ Θ.ν rtol = .02
+        @test νs[max_idx] ≈ Θ.ν rtol = 0.02
 
         ηs = range(0, Θ.η * 1.2, 100)
         LLs = map(η -> loglikelihood(Wald(; Θ..., η), data), ηs)
         _, max_idx = findmax(LLs)
-        @test ηs[max_idx] ≈ Θ.η atol = .05
+        @test ηs[max_idx] ≈ Θ.η atol = 0.05
 
-        αs = range(Θ.α * .8, Θ.α * 1.2, 100)
+        αs = range(Θ.α * 0.8, Θ.α * 1.2, 100)
         LLs = map(α -> loglikelihood(Wald(; Θ..., α), data), αs)
         _, max_idx = findmax(LLs)
-        @test αs[max_idx] ≈ Θ.α rtol = .02
+        @test αs[max_idx] ≈ Θ.α rtol = 0.02
 
-        τs = range(Θ.τ * .8, Θ.τ, 100)
+        τs = range(Θ.τ * 0.8, Θ.τ, 100)
         LLs = map(τ -> loglikelihood(Wald(; Θ..., τ), data), τs)
         _, max_idx = findmax(LLs)
-        @test τs[max_idx] ≈ Θ.τ rtol = .02
+        @test τs[max_idx] ≈ Θ.τ rtol = 0.02
     end
 
     @safetestset "simulate" begin