adnuts: No-U-Turn MCMC Sampling for 'ADMB' Models

#' adnuts: No-U-turn sampling for AD Model Builder (ADMB)
#'
#' 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 \pkg{adnuts} is based loosely off Stan and \R package
#' \pkg{rstan}
#'
#' The \pkg{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 \pkg{rstan} and \pkg{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, \pkg{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 \pkg{adnuts} included
#'   functionality for TMB models, but this has been replaced by
#'   \pkg{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.
#'
#' @docType package
#' @name adnuts
#' @import snowfall
#' @importFrom stats rnorm runif cov acf cor dnorm qnorm sd
#' @importFrom utils read.csv read.table write.table
#' @importFrom grDevices gray rgb
#' @importFrom graphics axis box hist legend lines mtext par plot
#'   points text abline
#' @importFrom ellipse ellipse
#' @importFrom rstan monitor
#' @importFrom ggplot2 ggplot aes geom_point theme_bw facet_wrap
## #' @importFrom withr defer
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