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FlexReg: an R package for fitting a general class of mixture regression models with bounded responses in a Bayesian framework

Author

Listed:
  • Roberto Ascari

    (University of Milano-Bicocca, Department of Economics, Management and Statistics)

  • Agnese Maria Di Brisco

    (University of Piemonte Orientale, Department of Studies for Economics and Business)

  • Sonia Migliorati

    (University of Milano-Bicocca, Department of Economics, Management and Statistics)

  • Andrea Ongaro

    (University of Milano-Bicocca, Department of Economics, Management and Statistics)

Abstract

Bounded responses, either continuous or discrete, are common in many fields, such as ecology, economics, and biomedical sciences. Standard regression models often fail to capture features like bimodality, heavy tails, overdispersion, or excess zeros, which frequently arise in these contexts. The FlexReg package, which provides a unified Bayesian framework for regression modeling of bounded outcomes, addresses all these challenges through flexible distributional assumptions and robust estimation based on Hamiltonian Monte Carlo implemented via the Stan language. The package implements beta-type and binomial-type models, along with their flexible and variance-inflated extensions, and allows for handling values on the boundary of the response support, when needed. Beyond model fitting, FlexReg includes tools for convergence diagnostics, posterior summaries, residual analysis, and predictive checking. By integrating and enriching recent methodological advances within a user-friendly Bayesian framework, this work delivers a computational infrastructure that facilitates the application of flexible regression methods for bounded data.

Suggested Citation

  • Roberto Ascari & Agnese Maria Di Brisco & Sonia Migliorati & Andrea Ongaro, 2026. "FlexReg: an R package for fitting a general class of mixture regression models with bounded responses in a Bayesian framework," Computational Statistics, Springer, vol. 41(2), pages 1-35, February.
    • Handle: RePEc:spr:compst:v:41:y:2026:i:2:d:10.1007_s00180-026-01720-y
    DOI: 10.1007/s00180-026-01720-y
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    References listed on IDEAS

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    1. Hinde, John & Demetrio, Clarice G. B., 1998. "Overdispersion: Models and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 27(2), pages 151-170, April.
    2. Jonah Gabry & Daniel Simpson & Aki Vehtari & Michael Betancourt & Andrew Gelman, 2019. "Visualization in Bayesian workflow," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 389-402, February.
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