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https://github.com/juliapsychometrics/raschmodels.jl
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.
https://github.com/juliapsychometrics/raschmodels.jl
item-response-theory julia rasch-model
Last synced: 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/juliapsychometrics/raschmodels.jl
- Owner: JuliaPsychometrics
- License: mit
- Created: 2022-10-06T12:09:43.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-20T11:33:46.000Z (5 months ago)
- Last Synced: 2024-08-20T13:36:58.479Z (5 months ago)
- Topics: item-response-theory, julia, rasch-model
- Language: Julia
- Homepage:
- Size: 207 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# RaschModels.jl
[](https://juliapsychometrics.github.io/RaschModels.jl/stable/)
[](https://juliapsychometrics.github.io/RaschModels.jl/dev/)
[](https://github.com/JuliaPsychometrics/RaschModels.jl/actions/workflows/CI.yml?query=branch%3Amain)
[](https://codecov.io/gh/JuliaPsychometrics/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](#roadmap)
for details.
## Installation
To install this package you can use Julias package management system.
```julia
] 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.
```julia
data = rand(0:1, 100, 5)
```
Using `data` as our response data we can fit a Rasch Model.
```julia
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.
```julia
rasch_bayes = fit(RaschModel, data, NUTS(), 1_000)
```
Additional plotting capabilities are provided by [ItemResponsePlots.jl](https://github.com/JuliaPsychometrics/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