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bayesnec - A Bayesian No-Effect- Concentration (NEC) Algorithm

Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>; Carpenter 'et al.' (2017)<doi:10.18637/jss.v076.i01> to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)<doi:10.1016/j.ecoenv.2009.09.012>), 'NSEC' (see Fisher and Fox (2023)<doi:10.1002/etc.5610>), and 'N(S)EC (see Fisher et al. 2023<doi:10.1002/ieam.4809>). A full description of this package can be found in Fisher 'et al.' (2024)<doi:10.18637/jss.v110.i05>. This package expands and supersedes an original version implemented in 'R2jags' (see Su and Yajima (2020)<https://CRAN.R-project.org/package=R2jags>; Fisher et al. (2020)<doi:10.5281/ZENODO.3966864>).

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bayesian-inferenceconcentration-responseecotoxicologyno-effect-concentrationnon-linear-decaythreshold-derivationtoxicology

7.46 score 16 stars 299 scripts 607 downloads

ssddata - Species Sensitivity Distribution Data

Reference data sets of species sensitivities to compare the results of fitting species sensitivity distributions using software such as 'ssdtools' and 'Burrlioz'. It consists of curated data sets for individual chemicals from Australian, New Zealand and Canadian organizations, several data sets from anonymous sources, and larger uncurated compilations drawn from the ANZTOX, WQBench and EnviroTox databases. It also includes a data set of the results of fitting various distributions using different software.

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dataspecies-sensitivity-distributionssdquarto

7.04 score 2 stars 8 dependents 19 scripts 534 downloads

FSSgam - Full Subsets Multiple Regression Using GAMs

Full-subsets information-theoretic approaches are increasingly used to explore predictive power and variable importance when a wide range of candidate predictors are being considered. This package provides functions that can be used to construct, fit, and compare a complete model set of possible ecological or environmental predictors for a given response variable of interest. Models are based on Generalized Additive Models (GAMs) and build on the 'MuMIn' package. Advantages include the capacity to fit more predictors than there are replicates, automatic removal of models with correlated predictors, and support for model sets that include interactions between factors and smooth predictors, as well as smooth-by-smooth interactions via te(). Methods are described in Fisher et al. (2018) <doi:10.1002/ece3.4134>.

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3.90 score 2 stars