marked: an R package for maximum likelihood and Markov Chain Monte Carlo analysis of capture–recapture data
Article first published online: 2 SEP 2013
Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
Methods in Ecology and Evolution
Volume 4, Issue 9, pages 885–890, September 2013
How to Cite
Laake, J. L., Johnson, D. S., Conn, P. B. (2013), marked: an R package for maximum likelihood and Markov Chain Monte Carlo analysis of capture–recapture data. Methods in Ecology and Evolution, 4: 885–890. doi: 10.1111/2041-210X.12065
- Issue published online: 2 SEP 2013
- Article first published online: 2 SEP 2013
- Accepted manuscript online: 30 APR 2013 08:31AM EST
- Manuscript Accepted: 23 APR 2013
- Manuscript Received: 21 FEB 2013
- Automatic Differentiation Model Builder;
- Markov Chain Monte Carlo;
- Population Analysis
- We describe an open-source r package, marked, for analysis of mark–recapture data to estimate survival and animal abundance.
- Currently, marked is capable of fitting Cormack–Jolly–Seber (CJS) and Jolly–Seber models with maximum likelihood estimation (MLE) and CJS models with Bayesian Markov Chain Monte Carlo methods. The CJS models can be fitted with MLE using optimization code in R or with Automatic Differentiation Model Builder. The latter allows incorporation of random effects.
- Some package features include: (i) individual-specific time intervals between sampling occasions, (ii) generation of optimization starting values from generalized linear model approximations and (iii) prediction of demographic parameters associated with unique combinations of individual and time-specific covariates.
- We demonstrate marked with a commonly analysed European dipper (Cinclus cinclus) data set.
- The package will be most useful to ecologists with large mark–recapture data sets and many individual covariates.