RaschModels.jl

RaschModels.jl is a Julia package for fitting and evaluating Rasch Models. It implements the basic Rasch Model, Partial Credit Model, and Rating Scale Model, as well as their linear extensions.

Note: Currently only a subset of models is available. Please see Roadmap for details.

Installation

To install this package you can use Julias package management system.

] add RaschModels

Getting started

Fitting a model using RaschModels.jl is easy. First, get some response data as a Matrix. In this example we just use some random data for 100 persons and 5 items.

data = rand(0:1, 100, 5)

Using data as our response data we can fit a Rasch Model.

rasch = fit(RaschModel, data, CML())

This function call fits the model using conditional Maximum Likelihood estimation. To fit the Rasch Model using Bayesian estimation just change the algorithm and provide the required additional arguments.

rasch_bayes = fit(RaschModel, data, NUTS(), 1_000)

Additional plotting capabilities are provided by ItemResponsePlots.jl.

Roadmap

RaschModels.jl is still under active development. Therefore, not all functionality is available yet. This roadmap provides a quick overview of the current state of the package.

Existing features

• Fitting Rasch Models (CML estimation, Bayesian estimation)
• Fitting Rating Scale Models (Bayesian estimation)
• Fitting Partial Credit Models (Bayesian estimation)
• Item response functions (all model types)
• Item information functions (all model types)
• Test response functions/Expected score functions (all model types)
• Test information functions (all model types)

Features in development

• Fitting Rating Scale Models via CML
• Fitting Partial Credit Models via CML
• Linear model extensions (Linear Logistic Test Model, Linear Rating Scale Model, Linear Partial Credit Model)
• Variational inference for Bayesian models

Planned features

• Model evaluation
• Model comparison