| Summary: | Abstract Capture–recapture methods are a common tool in ecological statistics, which have been extended to spatial capture–recapture models for data accompanied by location information. However, standard formulations of these models can be unwieldy and computationally intractable for large spatial scales, many individuals, and/or activity center movement. We provide a cumulative series of methods that yield dramatic improvements in Markov chain Monte Carlo (MCMC) estimation for two examples. These include removing unnecessary computations, integrating out latent states, vectorizing declarations, and restricting calculations to the locality of individuals. Our approaches leverage the flexibility provided by the nimble R package. In our first example, we demonstrate an improvement in MCMC efficiency (the rate of generating effectively independent posterior samples) by a factor of 100. In our second example, we reduce the computing time required to generate 10,000 posterior samples from 4.5 h down to five minutes, and realize an increase in MCMC efficiency by a factor of 25. These approaches can also be applied generally to other spatially indexed hierarchical models. We provide R code for all examples, an executable web‐appendix, and generalized versions of these techniques are made available in the nimbleSCR R package.
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