adnuts {adnuts}

adnuts: No-U-turn sampling for AD Model Builder (ADMB)

Description

Draw Bayesian posterior samples from an ADMB model using the no-U-turn MCMC sampler. Adaptation schemes are used so specifying tuning parameters is not necessary, and parallel execution reduces overall run time.

Details

The software package Stan pioneered the use of no-U-turn (NUTS) sampling for Bayesian models (Hoffman and Gelman 2014, Carpenter et al. 2017). This algorithm provides fast, efficient sampling across a wide range of models, including hierarchical ones, and thus can be used as a generic modeling tool (Monnahan et al. 2017). The functionality provided by adnuts is based loosely off Stan and R package rstan

The adnuts R package provides an R workflow for NUTS sampling for ADMB models (Fournier et al. 2011), including adaptation of step size and metric (mass matrix), parallel execution, and links to diagnostic and inference tools provided by rstan and shinystan. The ADMB implementation of NUTS code is bundled into the ADMB source itself (as of version 12.0). Thus, when a user builds an ADMB model the NUTS code is incorporated into the model executable. Thus, adnuts simply provides a convenient set of wrappers to more easily execute, diagnose, and make inference on a model. More details can be found in the package vignette.

Note that previous versions of adnuts included functionality for TMB models, but this has been replaced by tmbstan (Kristensen et al. 2016, Monnahan and Kristensen 2018).

References

Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M., Riddell, A., Guo, J.Q., Li, P., Riddell, A., 2017. Stan: A Probabilistic Programming Language. J Stat Softw. 76:1-29.

Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., Sibert, J., 2012. AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Method Softw. 27:233-249.

Hoffman, M.D., Gelman, A., 2014. The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J Mach Learn Res. 15:1593-1623.

Kristensen, K., Nielsen, A., Berg, C.W., Skaug, H., Bell, B.M., 2016. TMB: Automatic differentiation and Laplace approximation. J Stat Softw. 70:21.

Kristensen, K., 2017. TMB: General random effect model builder tool inspired by ADMB. R package version 1.7.11.

Monnahan, C.C., Thorson, J.T., Branch, T.A., 2017. Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods in Ecology and Evolution. 8:339-348.

Monnahan C.C., Kristensen K. (2018). No-U-turn sampling for fast Bayesian inference in ADMB and TMB: Introducing the adnuts and tmbstan R packages PLoS ONE 13(5): e0197954. https://doi.org/10.1371/journal.pone.0197954

Stan Development Team, 2016. Stan modeling language users guide and reference manual, version 2.11.0.

Stan Development Team, 2016. RStan: The R interface to Stan. R package version 2.14.1. http://mc-stan.org.