Implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell
    and modified Tukey non-additivity tests.

Implements the Bayesian Additive Voronoi Tessellation 
    model for non-parametric regression and machine learning as introduced
    in Stone and Gosling (2025) <doi:10.1080/10618600.2024.2414104>.
    This package provides a flexible alternative to BART (Bayesian Additive 
    Regression Trees) using Voronoi tessellations instead of trees. Users can 
    fit Bayesian regression models, estimate posterior distributions, and 
    visualise the resulting tessellations. It is particularly useful for spatial
    data analysis, machine learning regression, complex function approximation 
    and Bayesian modeling where the underlying structure is unknown. The method 
    is well-suited to capturing spatial patterns and non-linear relationships.

Methods for fitting identity-link GLMs and GAMs to discrete data,
    using EM-type algorithms with more stable convergence properties than standard methods.

Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).