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.

Connections between subpar dietary choices and negative health consequences are well established in the field of nutritional epidemiology. Consequently, in the United States, there is a standard practice of conducting regular surveys to evaluate dietary habits. One notable example is the National Health and Nutrition Examination Survey (NHANES) conducted every two years by the Center for Disease Control (CDC). Several scoring methods have been developed to assess the quality of diet in the overall population as well as different pertinent subgroups using dietary recall data collected in these surveys. The Healthy Eating Index (HEI) is one such metric, developed based on recommendations from the United States Department of Health and Human Services (HHS) and Department of Agriculture (USDA) and widely used by nutritionists. Presently, there is a scarcity of user-friendly statistical software tools implementing the scoring of these standard scoring metrics. Herein, we develop an R package heiscore to address this need. Our carefully designed package, with its many user-friendly features, increases the accessibility of the HEI scoring using three different methods outlined by the National Cancer Institute (NCI). Additionally, we provide functions to visualize multidimensional diet quality data via various graphing techniques, including bar charts and radar charts. Its utility is illustrated with many examples, including comparisons between different demographic groups.