#' BayesRTMB: Bayesian Modeling with RTMB
#'
#' @description
#' The `BayesRTMB` package provides an intuitive, R-based Bayesian modeling
#' environment using the `RTMB` package as its computational backend.
#' It allows users to perform Maximum A Posteriori (MAP) estimation,
#' Markov Chain Monte Carlo (MCMC) sampling, and Automatic Differentiation
#' Variational Inference (ADVI) for hierarchical and complex non-linear models
#' through a unified and user-friendly interface.
#'
#' @details
#' The standard workflow of the package consists of three main steps:
#'
#' 1. **Model Definition**: Use [rtmb_code()] to define the model structure
#'    (likelihood, prior distributions, transformations, etc.) using standard R syntax.
#' 2. **Model Instantiation**: Pass the data and the generated model code to
#'    [rtmb_model()] to create an `RTMB_Model` R6 object.
#' 3. **Estimation**: Call the appropriate method on the model object:
#'    * `$optimize()`: For MAP estimation and Laplace approximation.
#'    * `$sample()`: For MCMC sampling using the No-U-Turn Sampler (NUTS).
#'    * `$variational()`: For approximate Bayesian inference using ADVI.
#'
#' For more detailed examples and tutorials, please refer to the package vignettes.
#'
#' @seealso
#' Useful links:
#' * [https://github.com/norimune/BayesRTMB](https://github.com/norimune/BayesRTMB)
#' * Report bugs at [https://github.com/norimune/BayesRTMB/issues](https://github.com/norimune/BayesRTMB/issues)
#'
#' @keywords internal
"_PACKAGE"
