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 "Wikoff D, Ring C, DeVito M, Walker N, Birnbaum L, Haws L. Development and application of a systematic and quantitative weighting framework to evaluate the quality and relevance of relative potency estimates for dioxin-like compounds (DLCs) for human health risk assessment. Regul Toxicol Pharmacol. 2023 Dec;145:105500. doi: 10.1016/j.yrtph.2023.105500. Epub 2023 Oct 21. PMID: 37866700.". This dataset is associated with the following publication: Wikoff, D., C. Ring, M. Devito, N. Walker, L. Birnbaum, and L. Haws. Development and application of a systematic and quantitative weighting framework to evaluate the quality and relevance of relative potency estimates for dioxin-like compounds (DLCs) for human health risk assessment. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 145: 105500, (2023).

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