ExtendedRtIrtModeling

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Installation

This package isn’t registered in Julia yet, so you’ll need to download it from GitHub.

using Pkg
Pkg.add("https://github.com/jiewenTsai/ExtendedRtIrtModeling.jl")

or

]> add "https://github.com/jiewenTsai/ExtendedRtIrtModeling.jl"

Usage

using ExtendedRtIrtModeling

## creat a toy data
Cond = setCond(nSubj=1000, nItem=15)
truePara = setTrueParaMlIrt(Cond)
Data = setDataMlIrt(Cond, truePara)

## build a model and sample it!
MCMC = GibbsMlIrt(Cond, Data=Data, truePara=truePara)
sample!(MCMC)

## check the parameter recovery
getRmse(MCMC.truePara.b, MCMC.Post.mean.b)

If you have data to analyze, you can follow the following way,

using CSV, DataFrames

## import your data
Data = CSV.read("yourData.csv", DataFrame)
Cond = setCond(qRa=0.85, qRt=0.85, nChain=3, nIter=3000)
Data = InputData(
    Y=Matrix(Data[:,1:15]),
    T=exp.(Matrix(Data[:,16:30])),
    X=Matrix(Data[:,31:33])
)

## build a model and sample it!
MCMC = GibbsRtIrtQuantile(Cond, Data=Data)
sample!(MCMC)


MCMC.Post.mean.Σp
MCMC.Post.mean.β

How to Cite

If you use ExtendedRtIrtModeling.jl in your work, please cite using the reference given in CITATION.cff.

Contributing

If you want to make contributions of any kind, please first that a look into our contributing guide directly on GitHub or the contributing page on the website.

Contributors