unify wald model

Author: itsdfish

docs/make.jl

@@ -26,7 +26,6 @@ makedocs(
                 "Ex-Gaussian" => "ex_gaussian.md",
                 "Shifted Log Normal" => "shifted_lognormal.md",
                 "Wald" => "wald.md",
-                "Wald Mixture" => "wald_mixture.md"
             ],
             "Multi-choice Models" => [
                 "Single Attribute Models" => [

docs/src/wald.md

@@ -1,6 +1,6 @@
 # Wald Model
 
-The Wald model, also known as the inverse Gaussian, a sequential sampling model for single choice decisions. It is formally equivalent to a drift diffusion model with one decision threshold and no starting point or across Plots drift rate variability.
+The Wald model, also known as the inverse Gaussian, a sequential sampling model for single choice decisions. It is formally equivalent to a drift diffusion model with one decision threshold and no starting point or across  drift rate variability. The current implementation does include an inter-trial drift rate parameter $\eta$. When $\eta = 0$, the model reduces to the standard Wald model. 
 
 # Example
 In this example, we will demonstrate how to use the Wald model in a generic single choice decision task. 
@@ -21,7 +21,7 @@ using Random
 Random.seed!(8741)
 ```
 ## Create Model Object
-In the code below, we will define parameters for the Wald Model and create a model object to store the parameter values. 
+In the code below, we will define parameters for the Wald and create a model object to store the parameter values. 
 
 ### Drift Rate
 
@@ -30,6 +30,15 @@ The parameter $\nu$ represents the evidence accumulation rate.
 ```@example wald
 ν = 3.0
 ```
+
+### Drift Rate Variability
+
+The parameter $\eta$ represents the standard deviation of the evidence accumulation rate across trials.
+
+```@example wald
+η = 0.20
+```
+
 ### Threshold
 
 The parameter $\alpha$ the amount of evidence required to make a decision.
@@ -47,7 +56,7 @@ Non-decision time is an additive constant representing encoding and motor respon
 Now that values have been asigned to the parameters, we will pass them to `Wald` to generate the model object.
 
 ```@example wald 
-dist = Wald(ν, α, τ)
+dist = Wald(ν, η, α, τ)
 ```
 ## Simulate Model
 
@@ -57,7 +66,7 @@ Now that the model is defined, we will generate $10,000$ choices and reaction ti
 rts = rand(dist, 1000)
 ```
 
-## Compute  PDF
+## Compute PDF
 Similarly, the log PDF for each observation can be computed as follows:
 
  ```@example wald 
@@ -70,7 +79,6 @@ Similarly, the log PDF for each observation can be computed as follows:
  ```@example wald 
 logpdf.(dist, rts)
 ```
-
 ## Compute CDF
 The cumulative probability density $\Pr(T \leq t)$ is computed by passing the model and a value $t$ to `cdf`.
 
@@ -88,7 +96,7 @@ plot!(dist; t_range=range(.130, 1, length=100))
 
 Anders, R., Alario, F., & Van Maanen, L. (2016). The shifted Wald distribution for response time data analysis. Psychological methods, 21(3), 309.
 
-Folks, J. L., & Chhikara, R. S. (1978). The inverse Gaussian distribution and its statistical application—a review. Journal of the Royal Statistical Society: Series B (Methodological), 40(3), 263-275.
+Folks, J. L., & Chhikara, R. S. (1978). The inverse Gaussian distribution and its statistical application—a review. Journal of the Royal Statistical Society Series B: Statistical Methodology, 40(3), 263-275.
 
-Steingroever, H., Wabersich, D., & Wagenmakers, E. J. (2021). Modeling across-Plots variability in the Wald drift rate parameter. Behavior Research Methods, 53, 1060-1076.
+Steingroever, H., Wabersich, D., & Wagenmakers, E. J. (2021). Modeling across-trial variability in the Wald drift rate parameter. Behavior Research Methods, 53, 1060-1076.
 

docs/src/wald_mixture.md

@@ -67,7 +67,6 @@ export SSM2D
 export stDDM
 export ContinuousMultivariateSSM
 export Wald
-export WaldMixture
 
 export cdf
 export compute_choice_probs
@@ -99,8 +98,7 @@ include("utilities.jl")
 
 include("single_choice_models/ex_gaussian.jl")
 include("single_choice_models/ShiftedLogNormal.jl")
-include("single_choice_models/Wald.jl")
-include("single_choice_models/wald_mixture.jl")
+include("single_choice_models/wald.jl")
 
 include("multi_choice_models/AttentionalDiffusion.jl")
 include("multi_choice_models/ClassicMDFT.jl")