add rand for shiftedlognormal

Project.toml

@@ -1,7 +1,7 @@
 name = "SequentialSamplingModels"
 uuid = "0e71a2a6-2b30-4447-8742-d083a85e82d1"
 authors = ["itsdfish"]
-version = "0.11.3"
+version = "0.11.4"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

docs/make.jl

@@ -27,7 +27,7 @@ makedocs(
                 "Ex-Gaussian Distribution" => "ex_gaussian.md",
                 "Shifted LogNormal" => "shifted_lognormal.md",
                 "Wald Model" => "wald.md",
-                "Wald Mixture Model" => "wald_mixture.md",
+                "Wald Mixture Model" => "wald_mixture.md"
             ],
             "Multi-choice Models" => [
                 "Single Attribute Models" => [
@@ -37,17 +37,17 @@ makedocs(
                     "Lognormal Race Model (LNR)" => "lnr.md",
                     "Poisson Race" => "poisson_race.md",
                     "Racing Diffusion Model (RDM)" => "rdm.md",
-                    "Starting-time Drift Diffusion Model (stDDM)" => "stDDM.md",
+                    "Starting-time Drift Diffusion Model (stDDM)" => "stDDM.md"
                 ],
                 "Multi-attribute Models" => [
                     "Muti-attribute Attentional Drift Diffusion Model" => "maaDDM.md",
                     "Multi-attribute Decision Field Theory" => "mdft.md",
-                    "Multi-attribute Linear Ballistic Accumulator" => "mlba.md",
+                    "Multi-attribute Linear Ballistic Accumulator" => "mlba.md"
                 ]
             ],
             "Alternative Geometries" => [
-                "Circular Drift Diffusion Model (CDDM)" => "cddm.md",
-            ],
+                "Circular Drift Diffusion Model (CDDM)" => "cddm.md"
+            ]
         ],
         "Parameter Estimation" => [
             "Mode Estimation" => "mode_estimation.md",

src/ShiftedLogNormal.jl

@@ -47,9 +47,24 @@ function logpdf(dist::AbstractShiftedLogNormal, rt)
     return logpdf(LogNormal(ν, σ), rt - τ)
 end
 
-function rand(dist::AbstractShiftedLogNormal, n_trials::Int)
+function rand(rng::AbstractRNG, dist::AbstractShiftedLogNormal, n_trials::Int)
     (; τ, ν, σ) = dist
-    return rand(LogNormal(ν, σ), n_trials) .+ τ
+    return rand(rng, LogNormal(ν, σ), n_trials) .+ τ
 end
 
-model = ShiftedLogNormal(ν = 1, σ = 1, τ = 0.20)
+function rand(rng::AbstractRNG, dist::AbstractShiftedLogNormal)
+    (; τ, ν, σ) = dist
+    return rand(rng, LogNormal(ν, σ)) .+ τ
+end
+
+function params(d::AbstractShiftedLogNormal)
+    return (d.ν, d.σ, d.τ)
+end
+
+function mean(dist::AbstractShiftedLogNormal)
+    return mean(LogNormal(dist.ν, dist.σ)) + dist.τ
+end
+
+function std(dist::AbstractShiftedLogNormal)
+    return std(LogNormal(dist.ν, dist.σ))
+end

src/Wald.jl

@@ -1,5 +1,3 @@
-loglikelihood(d::AbstractWald, data::AbstractArray{T, 1}) where {T} = sum(logpdf.(d, data))
-
 """
     Wald{T<:Real} <: AbstractWald
 

src/type_system.jl

@@ -135,6 +135,7 @@ maximum(d::SSM1D) = Inf
 minimum(d::SSM2D) = 0.0
 maximum(d::SSM2D) = Inf
 
+insupport(d::SSM1D, rt::Real) = rt ≥ minimum(d) && rt ≤ maximum(d)
 insupport(d::SSM2D, data) = data.rt ≥ minimum(d) && data.rt ≤ maximum(d)
 
 Base.broadcastable(x::SSM1D) = Ref(x)
@@ -188,6 +189,18 @@ Computes the likelihood for a 2D sequential sampling model.
 logpdf(d::SSM2D, data::NamedTuple) = logpdf.(d, data.choice, data.rt)
 logpdf(d::SSM2D, data::AbstractVector{<:Real}) = logpdf(d, Int(data[1]), data[2])
 
+"""
+    loglikelihood(d::SSM1D, data::AbstractArray{T, 1})
+
+Computes the summed log likelihood for a 1D sequential sampling model. 
+
+# Arguments
+
+- `d::SSM2D`: an object for a 2D sequential sampling model 
+- `data::AbstractVector{<:Real}`: a vector of reaction times
+"""
+loglikelihood(d::SSM1D, data::AbstractVector{<:Real}) = sum(logpdf.(d, data))
+
 """
     loglikelihood(d::SSM2D, data::NamedTuple)