marked: an R package for maximum likelihood and Markov Chain Monte Carlo analysis of capture–recapture data

Summary 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 C ormack– J olly– S eber ( CJS ) and J olly– S eber models with maximum likelihood estimation ( MLE ) and CJS models with B ayesian M arkov C hain M onte C arlo methods. The CJS models can be fitted with MLE using optimization code in R or with A utomatic D ifferentiation M odel B uilder. 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 E uropean dipper ( Cinclus cinclus ) data set. The package will be most useful to ecologists with large mark–recapture data sets and many individual covariates.