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Fitting multievent models in R, Nimble and JAGS

by Olivier Gimenez

August 12, 2016 and December 19, 2019.

What it does

Here I provide R codes to fit multievent capture-recapture models (Pradel et al. 2005). Multievent models are hidden Markov models that are helpful in lots of situations to analyse capture-recapture data (see this list of applications for example).

I show how to obtain maximum-likelihood estimates using R and Bayesian estimates using Nimble and JAGS. Two examples are considered. First a simple Cormack-Jolly-Seber model is illustrated with the classical Dipper dataset (Pradel 2005; Gimenez et al. 2007). Second, a multistate model with uncertainty in the state assignement is illustrated with a dataset on Sooty shearwaters (Pradel 2005; Gimenez et al. 2012).

What it contains

Cormack-Jolly-Seber example using the Dipper dataset

  • cjs_nimble.R: Bayesian fitting using R and Nimble
  • cjs_jags.R: Bayesian fitting using R and JAGS
  • cjs_R.R: maximum-likelihood fitting using R
  • dipper.txt: the Dipper dataset

Multistate with uncertain state example using the Sooty shearwater dataset

  • uncertainty_nimble.R: Bayesian fitting using R and Nimble
  • uncertainty_jags.R: Bayesian fitting using R and JAGS
  • uncertainty_R.R: maximum-likelihood fitting using R
  • titis2.txt: the Sooty shearwater dataset

References

Gimenez, O., Lebreton, J.-D., Gaillard, J.-M., Choquet, R. and R. Pradel (2012). Estimating demographic parameters using hidden process dynamic models. Theoretical Population Biology 82: 307-316.

Gimenez, O., V. Rossi, R. Choquet, C. Dehais, B. Doris, H. Varella, J.-P. Vila and R. Pradel (2007). State-space modelling of data on marked individuals. Ecological Modelling 206: 431-438.

Pradel, R. (2005). Multievent: an extension of multistate capture–recapture models to uncertain states. Biometrics 61: 442–447.