itsdfish

using Turing
using SequentialSamplingModels
using Distributions

choice1,rt1 = rand(LBA(ν=[3.0, 2.0], A=0.8, k=0.2, τ=0.3), 25)
choice2,rt2 = rand(LBA(ν=[1.0, 1.5], A=0.7, k=0.2, τ=0.3), 25)

data = (choice=[choice1;choice2],rt=[rt1;rt1])

condition = repeat([1,0], inner=25)

itsdfish

using Distributions


function sample_prior()
    return rand(Beta(1, 1))
end

function generate_data()
    θ = sample_prior()
    y = rand(Binomial(10, θ))

itsdfish

### A Pluto.jl notebook ###
# v0.19.9

using Markdown
using InteractiveUtils

# ╔═╡ 46e9d6b2-fac9-11ec-2cd8-67797a9be080
using CommonMark, PlutoUI

# ╔═╡ bb080054-390d-491e-b412-fd9718a0c8c8

itsdfish

### A Pluto.jl notebook ###
# v0.16.1

using Markdown
using InteractiveUtils

# ╔═╡ 633f980c-2e8d-11ec-2fe5-57a90a261d20
begin
	using ACTRModels, PrettyTables
	import Base: show

itsdfish

[deps]
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
Memoization = "6fafb56a-5788-4b4e-91ca-c0cea6611c73"
ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

[compat]
Distributions = "0.25.0"
Memoization = "0.1.0"
ReverseDiff = "1.9.0"

itsdfish

cd(@__DIR__)
using Pkg
Pkg.activate("")

using CmdStan, Random, Distributions, MCMCChains

model = "
data { 
    // total number of data points in y
    int<lower=1> N;