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Benchmark Dose Tools

Benchmark Dose Software (BMDS) Version 3 Release History

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Released February 2019

BMDS 3.0 message box for running models

  • Numerous programmatic improvements and enhancements:
    • Fixed an issue with Akaike Informaton Criterion (AIC) derivation for continuous response models
    • Fixed issue where "Added Risk" and BMRF=0.001 caused slow performance for Dichotomous Hill and Bayesian MA models
    • Fixed issue with Test 3 and Individual Response Analysis for continuous models
    • Fixed issue with continuous models Likelihood of Interest reporting incorrect number of parameters
    • Fixed issue with continous models Test of Interest and p-values
    • Fixed a problem with running BMDS from a network drive
    • Fixed the Weibull model default parameter values and priors
    • Fixed miscellaneous issues with the nested logistic model
    • Fixed intermittent crashes with the Polynomial, Multistage and other models
    • Resolved a problem with A3 and R log-likelihood values, which are used to measure goodness-of-fit
    • Minor improvements

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Released September 30, 2018. 

  • Implemented in Microsoft Excel, with modeling computations performed in custom DLLs.
  • Fast analysis of multiple datasets and options
  • Enables users to see and specify modeling options in a single worksheet
  • Flexible documentation and report generation
  • Includes auto-selection features for identifying the “best” results in accordance with EPA recommendations or user-defined logic
  • Documents all inputs and outputs in a single results workbook for each dataset modeled
  • Provides flexible print options for displaying results in Microsoft Word tables formatted in a manner suitable for presentation in a risk assessment
  • Backwards compatible with prior BMDS versions
  • Bayesian versions of all traditional frequentist dichotomous models
  • Bayesian model averaging using all or weighted dichotomous models
  • Hybrid approach added to all continuous models
  • Lognormal response distribution added for all continuous models
  • ​All existing models have been re-coded to facilitate their maintenance and improve their performance in terms of stability, accuracy, reliability, and speed.

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