MixSIAR is an open-source R package (CRAN, GitHub) that helps you create and run Bayesian mixing models to analyze biotracer data (i.e. stable isotopes, fatty acids), following the MixSIAR model framework.

MixSIAR represents a collaborative coding project between the investigators behind MixSIR and SIAR: Brice Semmens, Brian Stock, Andrew Jackson, Eric Ward, Andrew Parnell, and Donald Phillips.

Why use MixSIAR?

  1. Consumer variability via covariate effects (i.e. allow consumers to not all have the same diet; Semmens et al. 2009; Francis et al. 2011; Parnell et al. 2013)
  2. Source uncertainty (fits source data within model, i.e. admit the sample mean is not the truth; Ward et al. 2010)
  3. Better error structures (Stock and Semmens 2016)
  4. Construct informative priors (deVries et al. 2016)
  5. Graphical User Interface (GUI) and script versions

These presentation slides illustrate the above points with examples.

MixSIAR model description

Stock BC, Jackson AL, Ward EJ, Parnell AC, Phillips DL, and Semmens BX. 2018. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 6:e5096. https://doi.org/10.7717/peerj.5096

Questions about how to use MixSIAR?

Before contacting me, please see the Issues tab on the GitHub page - you may find your question/answer already posted! If not, submit a new issue.