first batch of argument checks

Author: itsdfish

Project.toml

@@ -4,6 +4,7 @@ authors = ["itsdfish"]
 version = "0.13.0"
 
 [deps]
+ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 FunctionZeros = "b21f74c0-b399-568f-9643-d20f4fa2c814"
@@ -28,6 +29,7 @@ PlotsExt = "Plots"
 TuringExt = "Turing"
 
 [compat]
+ArgCheck = "2.5.0"
 Distributions = "v0.24.6, 0.25"
 DynamicPPL = "0.31.0,0.32.0,0.33.0,0.34.0,0.35.0,0.36.0"
 FunctionZeros = "0.2.0,0.3.0, 1"

src/SequentialSamplingModels.jl

@@ -6,6 +6,7 @@ https://itsdfish.github.io/SequentialSamplingModels.jl/dev/
 """
 module SequentialSamplingModels
 
+using ArgCheck
 using Distributions
 using FunctionZeros
 using PrettyTables

src/multi_choice_models/AttentionalDiffusion.jl

@@ -6,11 +6,11 @@ An object for the attentional diffusion model.
 # Parameters 
 
 - `ν::Vector{T}`: relative decision values (i.e., drift rates). ν ∈ ℝⁿ.
-- `σ::T`: standard deviation of noise in evidence accumulation. σ ∈ ℝ.
-- `Δ::T`: constant of evidence accumulation speed (evidence per ms). Δ ∈ ℝ.
-- `α::T`: evidence threshold. α ∈ ℝ.  
-- `z::T`: initial evidence. z ∈ [0, α] 
-- `θ::T`: bias towards attended alternative (lower indicates more bias)
+- `σ::T`: standard deviation of noise in evidence accumulation. σ ∈ ℝ⁺.
+- `Δ::T`: constant of evidence accumulation speed (evidence per ms). Δ ∈ ℝ⁺.
+- `α::T`: evidence threshold. α ∈ ℝ⁺.  
+- `z::T`: initial evidence. z ∈ [-α, α] 
+- `θ::T`: bias towards attended alternative (lower indicates more bias) Δ ∈ ℝ⁺
 - `τ::T`: non-decision time. τ ∈ [0, min_rt]
 
 # Constructors
@@ -71,6 +71,16 @@ struct aDDM{T <: Real} <: AbstractaDDM
     α::T
     z::T
     τ::T
+
+    function aDDM(ν::Vector{T}, σ::T, Δ::T, θ::T, α::T, z::T, τ::T) where {T <: Real}
+        @argcheck σ ≥ 0
+        @argcheck Δ ≥ 0
+        @argcheck θ ≥ 0
+        @argcheck α ≥ 0
+        @argcheck abs(z) ≤ α
+        @argcheck τ ≥ 0
+        return new{T}(ν, σ, Δ, θ, α, z, τ)
+    end
 end
 
 function aDDM(ν, σ, Δ, θ, α, z, τ)
@@ -286,10 +296,8 @@ function increment(rng::AbstractRNG, dist::aDDM, location)
         # option 2
     elseif location == 2
         return -Δ * (ν[2] - θ * ν[1]) + noise(rng, σ)
-    else
-        return noise(rng, σ)
     end
-    return -100.0
+    return noise(rng, σ)
 end
 
 noise(rng, σ) = rand(rng, Normal(0, σ))

src/multi_choice_models/LBA.jl

@@ -41,6 +41,13 @@ mutable struct LBA{T <: Real, T1 <: Union{<:T, Vector{<:T}}} <: AbstractLBA{T, T
     A::T
     k::T
     τ::T
+    function LBA(ν::Vector{T}, σ::T1, A::T, k::T, τ::T) where {T <: Real, T1 <: Union{<:T, Vector{<:T}}}
+        @argcheck all(σ .≥ 0)
+        @argcheck A ≥ 0
+        @argcheck k ≥ 0
+        @argcheck τ ≥ 0
+        return new{T,T1}(ν, σ, A, k, τ)
+    end
 end
 
 function LBA(ν, σ, A, k, τ::T) where {T}

src/multi_choice_models/LNR.jl

@@ -36,6 +36,11 @@ struct LNR{T <: Real, T1 <: Union{<:T, Vector{<:T}}} <: AbstractLNR{T, T1}
     ν::Vector{T}
     σ::T1
     τ::T
+    function LNR(ν::Vector{T}, σ::T1, τ::T) where {T <: Real, T1 <: Union{<:T, Vector{<:T}}}
+        @argcheck all(σ .≥ 0)
+        @argcheck τ ≥ 0
+        return new{T,T1}(ν, σ, τ)
+    end
 end
 
 function LNR(ν, σ, τ::T) where {T}

src/multi_choice_models/MLBA.jl

@@ -69,6 +69,17 @@ mutable struct MLBA{T <: Real, T1 <: Union{<:T, Vector{<:T}}} <: AbstractMLBA{T,
     A::T
     k::T
     τ::T
+    function MLBA(ν::Vector{T}, β₀::T, λₚ::T, λₙ::T, γ::T, σ::T1, A::T, k::T, τ::T) where {T <: Real, T1 <: Union{<:T, Vector{<:T}}}
+        @argcheck β₀ ≥ 0
+        @argcheck λₚ ≥ 0
+        @argcheck λₙ ≥ 0
+        @argcheck γ ≥ 0
+        @argcheck σ ≥ 0
+        @argcheck A ≥ 0
+        @argcheck k ≥ 0
+        @argcheck τ ≥ 0
+        return new{T,T1}(ν, β₀, λₚ, λₙ, γ, σ, A, k, τ)
+    end
 end
 
 function MLBA(ν, β₀, λₚ, λₙ, γ, σ, A, k::T, τ) where {T}

src/multi_choice_models/RDM.jl

@@ -119,6 +119,13 @@ struct RDM{T <: Real} <: AbstractRDM
     A::T
     k::T
     τ::T
+
+    function RDM(ν::Vector{T}, A::T, k::T, τ::T) where {T <: Real}
+        @argcheck A ≥ 0
+        @argcheck k ≥ 0
+        @argcheck τ ≥ 0
+        return new{T}(ν, A, k, τ)
+    end
 end
 
 function RDM(ν, A, k::T, τ) where {T}

src/multi_choice_models/poisson_race.jl

@@ -34,6 +34,12 @@ struct PoissonRace{T <: Real} <: AbstractPoissonRace
     ν::Vector{T}
     α::Vector{Int}
     τ::T
+    function PoissonRace(ν::Vector{T}, α::Vector{Int}, τ::T) where {T <: Real}
+        @argcheck length(ν) == length(α)
+        @argcheck all(α .≥ 0)
+        @argcheck τ ≥ 0
+        return new{T}(ν, α, τ)
+    end
 end
 
 function PoissonRace(ν, α, τ::T) where {T}

src/single_choice_models/ShiftedLogNormal.jl

@@ -36,6 +36,11 @@ struct ShiftedLogNormal{T <: Real} <: AbstractShiftedLogNormal
     ν::T
     σ::T
     τ::T
+    function ShiftedLogNormal(ν::T, σ::T, τ::T) where {T <: Real}
+        @argcheck σ ≥ 0
+        @argcheck τ ≥ 0
+        return new{T}(ν, σ, τ)
+    end
 end
 
 ShiftedLogNormal(ν, σ, τ) = ShiftedLogNormal(promote(ν, σ, τ)...)

src/single_choice_models/Wald.jl

@@ -37,6 +37,11 @@ struct Wald{T <: Real} <: AbstractWald
     ν::T
     α::T
     τ::T
+    function Wald(ν::T, α::T, τ::T) where {T <: Real}
+        @argcheck α ≥ 0
+        @argcheck τ ≥ 0
+        return new{T}(ν, α, τ)
+    end
 end
 
 Wald(; ν = 1.5, α = 0.75, τ = 0.20) = Wald(ν, α, τ)