Journal of Data Science

Density deconvolution is a fundamental problem in measurement error settings, arising when the goal is to recover the distribution of unobserved latent variables based on their error-contaminated proxies. An important application arises in nutritional epidemiology, where the objective is to estimate the long-term average intake of nutritional components using 24-hour dietary recall data. This task involves complex multivariate models that account for conditionally heteroscedastic measurement errors, zero-inflated proxies for episodically consumed dietary components, diverse marginal distribution shapes across components, etc. While flexible Bayesian hierarchical methods, coupled with Markov chain Monte Carlo techniques, have been increasingly successful in recent years to enable deconvolution under such intricate but realistic scenarios, the development of rapid and user-friendly software implementations has lagged behind. The $\mathsf{R}$ package BayesDecon has been developed to address this challenge, providing a fast and accessible implementation of flexible Bayesian deconvolution methods for practitioners working with measurement errors. In the process, we have introduced substantial improvements to several aspects of the original models and algorithms, and have also added options to fit simpler parametric models. Although illustrated on challenges in nutritional epidemiology, the package addresses general deconvolution problems and hence is broadly applicable to other domains as well.