:snake: IXPLORE released on PyPI and technical report on arXiv! :tada:

IXPLORE is a Python package for bounded ideal point estimation with grid-based uncertainty quantification (Bachmann, 2026), now available on PyPI!

IXPLORE jointly embeds users and questionnaire items in a shared 2D latent space: each item defines a logistic decision boundary, and each user receives a posterior distribution over the bounded map. On five benchmark datasets spanning surveys, roll calls, and deliberation, IXPLORE surpasses IRT- and ML-based algorithms on reconstruction and imputation error, especially for users with sparse responses. The accompanying technical report is available on arXiv, and the source code is on GitHub.

Install it with pip install ixplore and give it a try!

Ideal point estimation is widely used to analyze and visualize political data. However, selecting the corresponding spatial model involves various trade-offs: while Item Response Theory (IRT) approaches are based on utility functions rather than optimized for predictive accuracy, most Machine Learning (ML) alternatives struggle to generalize beyond training data when embedding sparse test responses. We introduce IXPLORE, a bounded ideal point estimation algorithm that combines a predictive fit objective with a sparsity-aware likelihood function. On five benchmark datasets spanning surveys, roll calls, and deliberation, this approach surpasses IRT- and ML-based algorithms on reconstruction and imputation error – especially for users with sparse responses. Furthermore, we show that non-linear feature transforms can further reduce the reconstruction error while remaining visually interpretable. To quantify uncertainty, IXPLORE applies grid-based posterior inference on a bounded 2D latent space. Available as a Python package on PyPI, IXPLORE offers a flexible framework for constructing bounded, interpretable political maps with fast inference and strong imputation performance.