covr, testthat, spelling, knitr, rmarkdown, ggforce,
RcppArmadillo, shinyWidgets, xts, htmltools, shinythemes,
progressr

Methods for assessing animal movement from telemetry and biologging
data using non-parametric Bayesian methods. This includes features for pre-
processing and analysis of data, as well as the visualization of results
from the models. This framework does not rely on standard parametric density
functions, which provides flexibility during model fitting. Further details
regarding part of this framework can be found in Cullen et al. (2021) <doi:10.1101/2020.11.05.369702>.
covr, testthat, spelling, knitr, rmarkdown, ggforce,
RcppArmadillo, xts, htmltools, shinythemes, DT, datamods,
viridis

A Bayesian No-Effect- Concentration (NEC) Algorithm
c(person("Rebecca", "Fisher", email = "r.fisher@aims.gov.au", role = c("aut", "cre")), person("Diego","Barneche",role="aut"), person("Gerard","Ricardo",role="aut"), person("David","Fox",role="aut"))
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>. This package expands and supersedes an original version implemented in R2jags, see Fisher, Ricardo and Fox (2020)<doi:10.5281/ZENODO.3966864>.
loo, extraDistr, dplyr, tidyr, purrr, tidyselect, lazyeval,
ggplot2, evaluate, rlang
rstan, knitr, rmarkdown, testthat (>= 3.0.0)
Rebecca Fisher [aut, cre],
Diego Barneche [aut],
Gerard Ricardo [aut],
David Fox [aut]

formula.tools, loo, extraDistr, dplyr, tidyr, purrr,
tidyselect, evaluate, rlang

Numerical Integration for Bayesian Inference
Alan Genz (BAYESPACK Fortran source code) and Bjoern Bornkamp
(R interface and minor adaptions in Fortran source code)
The bayespack package provides an R interface to the
Fortran BAYESPACK integration routines written by Alan Genz.
Given an unnormalized posterior distribution, the BAYESPACK
routines approximate the normalization constant and the mean
and covariance of the posterior. Posterior means of additional
functions of the parameters can be calculated as well.