ReviewCurrent environmental health reports2026
A Review of R Packages for Bayesian Model-based Clustering of High-dimensional Multivariate Environmental Exposures.
Review in Current environmental health reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
2 authors.
Funding
Abstract
purpose of reviewThe analysis of a collective set of environmental health exposures in large populations can be complex due to its dimensionality, correlation between exposures, and skewed data structures. Bayesian model-based clustering has gained traction for its computational advantages and model flexibility for the analysis of big data. This review aims to highlight R packages available to derive environmental exposure patterns in heterogeneous populations using a Bayesian mixture model framework. RECENT
findingsTwenty-one packages have been developed since 2006 with the capability of analyzing multivariate exposures, with more than half being released in the last ten years. Packages are available to identify both cross-sectional and longitudinal exposure patterns from a joint set of continuous, binary, categorical, directional, and ranked data types. Bayesian nonparametric priors are available to determine the appropriate number of latent clusters to fit. Variable selection is also available in some packages to focus primarily on exposures that may drive underlying patterns present. Bayesian mixture models are an attractive solution to analyze joint environmental exposures and its subsequent impact on population health. While software packages have been developed to provide researchers with advanced tools and models suitable for data complexities seen in environmental exposure data, their applications in the literature is significantly low. Further awareness of software packages and their capabilities could increase utilization and improve analysis of environmental exposures and population health.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.