p-gw

S1WantCurse	S1WantScold	S1WantShout	S2WantCurse	S2WantScold	S2WantShout	S3WantCurse	S3WantScold	S3WantShout	S4WantCurse	S4WantScold	S4WantShout	S1DoCurse	S1DoScold	S1DoShout	S2DoCurse	S2DoScold	S2DoShout	S3DoCurse	S3DoScold	S3DoShout	S4DoCurse	S4DoScold	S4DoShout	Anger	Sex
0	0	0	0	0	0	0	0	1	1	0	0	1	0	1	1	0	0	1	0	0	1	1	1	20	1
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	11	1
1	1	1	1	0	1	1	0	0	0	0	0	0	1	1	0	0	1	0	0	0	1	0	0	17	0
1	1	1	1	1	1	1	0	0	0	0	0	1	1	1	1	1	1	1	0	0	0	0	0	21	0
1	0	1	1	0	0	1	0	0	1	0	0	1	1	0	1	0	0	1	0	0	1	0	0	17	0
1	1	0	1	0	0	1	1	1	0	0	0	1	0	0	1	0	0	1	0	0	1	1	0	21	0
1	1	1	1	1	1	1	0	1	0	0	0	1	1	1	1	1	1	0	0	0	0	0	0	39	0
0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	21	0
0	0	0	1	1	0	1	0	1	1	0	0	1	0	0	1	0	0	1	0	1	0	1	0	24	0

p-gw

using Distributions, Turing, ReverseDiff, Memoization, LogExpFunctions
using BenchmarkTools
using Turing: @addlogprob!
using StanSample

Turing.setprogress!(false)
Turing.setadbackend(:reversediff)
Turing.setrdcache(true)

set_cmdstan_home!(homedir() * "/Applications/cmdstan/")

p-gw

using IRT, CSV, DataFrames

url = "https://raw.githubusercontent.com/t-alfers/Rasch-Turing/master/dichotom.txt"
data = CSV.read(download(url), DataFrame, delim = "\t")
data = data[:, Not([:Anger, :Sex])]
data.person = collect(1:nrow(data))

m = model(OneParameterLogistic, data, Not(:person), :person)

# simulate data