1.2.1

PyPI

adaptivetesting

adaptivetesting is a Python package that can be used to simulate and evaluate custom CAT scenarios as well as implement them in real-world testing scenarios from a single codebase

Published29 Jun 2026

Direct dependencies6

Transitive dependenciesNot calculated

README

Version 1.2.1 documentation

adaptivetesting

An open-source Python package for simplified, customizable Computerized Adaptive Testing (CAT) using Bayesian methods.

Key Features

Bayesian Methods: Built-in support for Bayesian ability estimation with customizable priors
Flexible Architecture: Object-oriented design with abstract classes for easy extension
Item Response Theory: Full support for 1PL, 2PL, 3PL, and 4PL models, GRM, GPCM
Multiple Estimators:
- Maximum Likelihood Estimation (MLE)
- Bayesian Modal Estimation (BM)
- Expected A Posteriori (EAP)
Item Selection Strategies: Maximum information criterion
Content Balancing: Maximum Priority Index, Weighted Penalty Model
Exposure Control: Randomesque Item Selection, Maximum Priority Index
Simulation Framework: Comprehensive tools for CAT simulation and evaluation
Real-world Application: Direct transition from simulation to production testing
Stopping Criteria: Support for standard error and test length criteria
Data Management: Built-in support for CSV and pickle data formats

Installation

Install from PyPI using pip:

pip install adaptivetesting

For the latest development version:

pip install git+https://github.com/condecon/adaptivetesting

Documentation

You can find our documentation in the GitHub wiki.

Contributing

We welcome contributions! Please see our GitHub repository for:

Issue tracking
Feature requests
Pull request guidelines
Development setup

Research and Applications

This package is designed for researchers and practitioners in:

Educational assessment
Psychological testing
Cognitive ability measurement
Adaptive learning systems
Psychometric research

The package facilitates the transition from research simulation to real-world testing applications without requiring major code modifications.

Citation

If you use this package for your academic work, please provide the following reference: Engicht, J., Bee, R. M., & Koch, T. (2025). Customizable Bayesian Adaptive Testing with Python – The adaptivetesting Package. Open Science Framework. https://doi.org/10.31219/osf.io/d2xge_v1

@online{engichtCustomizableBayesianAdaptive2025,
  title = {Customizable {{Bayesian Adaptive Testing}} with {{Python}} – {{The}} Adaptivetesting {{Package}}},
  author = {Engicht, Jonas and Bee, R. Maximilian and Koch, Tobias},
  date = {2025-08-06},
  eprinttype = {Open Science Framework},
  doi = {10.31219/osf.io/d2xge_v1},
  url = {https://osf.io/d2xge_v1},
  pubstate = {prepublished}
}

License

This project is licensed under the terms specified in the LICENSE file.

Downloads & popularity

Awaiting observations

No metric observations yet

Download counts and popularity will appear here as the collector receives them.

Declared dependencies 6

Native requirements

6 direct targetsNot calculated transitive count

Package / target	Requirement	Relationship
numpy Environment { "extras": "", "marker": null, "requires_python": ">=3.12", "evaluation": "NOT_EVALUATED" }	`numpy>=2.1.0`	runtime
pandas Environment { "extras": "", "marker": null, "requires_python": ">=3.12", "evaluation": "NOT_EVALUATED" }	`pandas>=2.3.0`	runtime
scipy Environment { "extras": "", "marker": null, "requires_python": ">=3.12", "evaluation": "NOT_EVALUATED" }	`scipy>=1.15.0`	runtime
tqdm Environment { "extras": "", "marker": null, "requires_python": ">=3.12", "evaluation": "NOT_EVALUATED" }	`tqdm>=4.66.0`	runtime
matplotlib Environment { "extras": "", "marker": null, "requires_python": ">=3.12", "evaluation": "NOT_EVALUATED" }	`matplotlib>=3.10.0`	runtime
numdifftools Environment { "extras": "", "marker": null, "requires_python": ">=3.12", "evaluation": "NOT_EVALUATED" }	`numdifftools>=0.9.42`	runtime