Package: simmr 0.5.2

simmr: A Stable Isotope Mixing Model

Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.

Authors:Emma Govan [aut], Andrew Parnell [cre, aut]

simmr_0.5.2.tar.gz
simmr_0.5.2.zip(r-4.7)simmr_0.5.2.zip(r-4.6)simmr_0.5.2.zip(r-4.5)
simmr_0.5.2.tgz(r-4.6-x86_64)simmr_0.5.2.tgz(r-4.6-arm64)simmr_0.5.2.tgz(r-4.5-x86_64)simmr_0.5.2.tgz(r-4.5-arm64)
simmr_0.5.2.tar.gz(r-4.7-arm64)simmr_0.5.2.tar.gz(r-4.7-x86_64)simmr_0.5.2.tar.gz(r-4.6-arm64)simmr_0.5.2.tar.gz(r-4.6-x86_64)
simmr_0.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
simmr/json (API)

# Install 'simmr' in R:
install.packages('simmr', repos = c('https://andrewcparnell.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/andrewcparnell/simmr/issues

Pkgdown/docs site:https://andrewcparnell.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
Datasets:
  • geese_data - Geese stable isotope mixing data set
  • geese_data_day1 - A smaller version of the Geese stable isotope mixing data set
  • simmr_data_1 - A simple fake stable isotope mixing data set
  • simmr_data_2 - A 3-isotope fake stable isotope mixing data set
  • square_data - An artificial data set used to indicate effect of priors

On CRAN:

Conda:

openblascppjags

7.65 score 36 stars 83 scripts 977 downloads 12 mentions 9 exports 65 dependencies

Last updated from:f27bb42742. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK265
linux-devel-x86_64OK252
source / vignettesOK284
linux-release-arm64OK231
linux-release-x86_64OK251
macos-release-arm64OK205
macos-release-x86_64OK452
macos-oldrel-arm64OK205
macos-oldrel-x86_64OK371
windows-develOK226
windows-releaseOK226
windows-oldrelOK227
wasm-releaseOK174

Exports:combine_sourcescompare_groupscompare_sourcesposterior_predictiveprior_vizsimmr_elicitsimmr_ffvbsimmr_loadsimmr_mcmc

Dependencies:abindbackportsbayesmbayesplotbootcheckmateclicodacompositionscpp11crayonDEoptimRdistributionaldplyrfarverforcatsgenericsGGallyggplot2ggridgesggstatsgluegridExtragtablehmsisobandlabelinglatticelifecyclemagrittrMASSmatrixStatsnumDerivpatchworkpillarpkgconfigplyrposteriorprettyunitsprogresspurrrR2jagsR2WinBUGSR6RColorBrewerRcppRcppArmadilloRcppDistreshape2rjagsrlangrobustbaseS7scalesstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisviridisLitewithr

Stable Isotope Mixing Models in R with simmr
Introduction | Installation of the simmr package | Considerations before running simmr | Working with scripts | Data Structure | How to run simmr | Step 1: Getting the data into simmr | Step 2: Plotting the data in iso-space | Step 3: Running simmr | Step 4: Checking the algorithm converged | Step 5: Checking the model fit | Step 6: Exploring the results | How to run simmr on multiple groups | Combining sources | Running simmr with only one isotope | Setting up your own prior distributions | Customising plots | Other advanced use of simmr | Appendix - suggested reading
Rendered fromsimmr.Rmdusingknitr::rmarkdown

Last update: 2026-04-30
Started: 2015-08-07

simmr: advanced plotting guide
Introduction | A basic simmr run | Customisation of iso-space plots | Customisation of output plots | Adding in convex hulls to iso-space plots
Rendered fromadvanced_plotting.Rmdusingknitr::rmarkdown

Last update: 2023-10-13
Started: 2021-09-16

simmr: quick start guide
Step 1: install simmr | Step 2: load in the data | Step 3: load the data into simmr | Step 4: plot the data | Step 5: run through simmr and check convergence | Step 6: look at the output
Rendered fromquick_start.Rmdusingknitr::rmarkdown

Last update: 2023-10-02
Started: 2017-08-27