@Article{Meehan2017,
  author      = {Timothy D. Meehan and Nicole L. Michel and Håvard Rue},
  title       = {Estimating animal abundance with N-mixture models using the R-INLA package for R},
  date        = {2017-05-03},
  eprint      = {http://arxiv.org/abs/1705.01581v1},
  eprintclass = {stat.AP},
  eprinttype  = {arXiv},
  abstract    = {Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys. N-mixture models enable quantification of detection probability and often produce abundance estimates that are less biased. The purpose of this study was to demonstrate the use of the R-INLA package to analyze N-mixture models and to compare performance of R-INLA to two other common approaches -- JAGS (via the runjags package), which uses Markov chain Monte Carlo and allows Bayesian inference, and unmarked, which uses Maximum Likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models when (1) familiar model syntax and data format (relative to other R packages) are desired, (2) survey level covariates of detection are not essential, (3) fast computing times are necessary (R-INLA is 10 times faster than unmarked, 300 times faster than JAGS), and (4) Bayesian inference is preferred.},
  file        = {:Estimating_animal_abundance_with_N-mixture_models_.pdf:PDF;:http\://arxiv.org/pdf/1705.01581v1:PDF},
  keywords    = {stat.AP},
}

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