Data and files for "Stanfield, Z., Setzer, R.W., Hull, V. et al. Bayesian inference of chemical exposures from NHANES urine biomonitoring data. J Expo Sci Environ Epidemiol 32, 833–846 (2022). https://doi.org/10.1038/s41370-022-00459-0"

The bayesnec package has been developed for R to fit concentration (dose)-response curves (CR) to toxicity data for the purpose of deriving no-effect-concentration (NEC), no-significant-effect-concentration (NSEC), and effect-concentration (of specified percentage "x", ECx) thresholds from non-linear models fitted using Bayesian Hamiltonian Monte Carlo (HMC) via R packages brms and rstan or cmdstanr. In bayesnec it is possible to fit a single model, custom model-set, specific model-set or all of the available models. When multiple models are specified, the bnec() function returns a model weighted average estimate of predicted posterior values. A range of support functions and methods is also included to work with the returned single, or multi-model objects that allow extraction of raw, or model averaged predicted, NEC, NSEC and ECx values and to interrogate the fitted model or model-set. By combining Bayesian methods with model averaging, bayesnec provides a single estimate of toxicity and associated uncertainty that can be directly integrated into risk assessment frameworks. For full details see: Fisher, R., Barneche, D. R., Ricardo, G. F., & Fox, D. R. (2024). bayesnec: An R Package for Concentration-Response Modeling and Estimation of Toxicity Metrics. Journal of Statistical Software, 110(5), 1–41. https://doi.org/10.18637/jss.v110.i05

Supplementary data for "Ring C, Blanchette A, Klaren WD, Fitch S, Haws L, Wheeler MW, DeVito M, Walker N, Wikoff D. A multi-tiered hierarchical Bayesian approach to derive toxic equivalency factors for dioxin-like compounds. Regul Toxicol Pharmacol. 2023 Sep;143:105464. doi: 10.1016/j.yrtph.2023.105464. Epub 2023 Jul 27. PMID: 37516304.". Portions of this dataset are inaccessible because: Available on request from Daniele Wikoff. They can be accessed through the following means: Available on request from dwikoff@toxstrategies.com. Format: N/A. This dataset is associated with the following publication: Ring, C., A. Blanchette, W. Klaren, S. Fitch, L. Haws, M. Wheeler, M. Devito, N. Walker, and D. Wikoff. A multi-tiered hierarchical Bayesian approach to derive toxic equivalency factors for dioxin-like compounds. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 143: 105464, (2023).

Supplementary data for "Ring C, Blanchette A, Klaren WD, Fitch S, Haws L, Wheeler MW, DeVito M, Walker N, Wikoff D. A multi-tiered hierarchical Bayesian approach to derive toxic equivalency factors for dioxin-like compounds. Regul Toxicol Pharmacol. 2023 Sep;143:105464. doi: 10.1016/j.yrtph.2023.105464. Epub 2023 Jul 27. PMID: 37516304.". Portions of this dataset are inaccessible because: Available on request from Daniele Wikoff. They can be accessed through the following means: Available on request from dwikoff@toxstrategies.com. Format: N/A. This dataset is associated with the following publication: Ring, C., A. Blanchette, W. Klaren, S. Fitch, L. Haws, M. Wheeler, M. Devito, N. Walker, and D. Wikoff. A multi-tiered hierarchical Bayesian approach to derive toxic equivalency factors for dioxin-like compounds. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 143: 105464, (2023).

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).

Chemical concentrations, exposures, health risks by census tract for the United States from National Scale Air Toxics Assessment (NATA). This dataset is associated with the following publication: Huang, H., and T. Barzyk. Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates. International Journal of Environmental Research and Public Health. Molecular Diversity Preservation International, Basel, SWITZERLAND, 14(1): 1-15, (2017).

Chemical concentrations, exposures, health risks by census tract for the United States from National Scale Air Toxics Assessment (NATA). This dataset is associated with the following publication: Huang, H., and T. Barzyk. Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates. International Journal of Environmental Research and Public Health. Molecular Diversity Preservation International, Basel, SWITZERLAND, 14(1): 1-15, (2017).

Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747"