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BLMEngineInR
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Download and install BLMEngineInR package within the R console
Install from CRAN:
install.packages("BLMEngineInR")
Install from Github:
library("remotes")
install_github("cran/BLMEngineInR") Install by package version:
library("remotes")
install_version("BLMEngineInR", "0.1.7") Attach the package and use:
library("BLMEngineInR")
Maintained by
Kelly Croteau
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-09-15
Latest Update: 2025-09-15
Description:
A chemical speciation and toxicity prediction model for the toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) engine was originally programmed in 'PowerBasic' by Robert Santore and others. The main way the BLM can be used is to predict the toxicity of a metal to an organism with a known sensitivity (i.e., it is known how much of that metal must accumulate on that organism's biotic ligand to cause a physiological effect in a certain percentage of the population, such as a 20% loss in reproduction or a 50% mortality rate). The second way the BLM can be used is to estimate the chemical speciation of the metal and other constituents in water, including estimating the amount of metal accumulated to an organism's biotic ligand during a toxicity test. In the first application of the BLM, the amount of metal associated with a toxicity endpoint, or regulatory limit will be predicted, while in the second application, the amount of metal is known and the portions of that metal that exist in various forms will be determined. This version of the engine has been re-structured to perform the calculations in a different way that will make it more efficient in R, while also making it more flexible and easier to maintain in the future. Because of this, it does not currently match the desktop model exactly, but we hope to improve this comparability in the future.
How to cite:
Kelly Croteau (2025). BLMEngineInR: Biotic Ligand Model Engine. R package version 0.1.7, https://cran.r-project.org/web/packages/BLMEngineInR. Accessed 04 Jun. 2026.
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