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Rasch modeling with all the bells and whistles. Implementations for Rasch model, partial credit model, rating scale model, and its linear extensions (upcoming). Classical and Bayesian estimation.

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JuliaPsychometrics/RaschModels.jl

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RaschModels.jl

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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

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Rasch modeling with all the bells and whistles. Implementations for Rasch model, partial credit model, rating scale model, and its linear extensions (upcoming). Classical and Bayesian estimation.

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