## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)

## ----eval=FALSE---------------------------------------------------------------
# library(BayesRTMB)
# 
# code <- rtmb_code(
#   setup = {
#     # Data preprocessing and constant definitions
#   },
#   parameters = {
#     # Declaration of parameters to be estimated
#   },
#   transform = {
#     # Calculation of derived variables using parameters and data
#   },
#   model = {
#     # Definition of priors and likelihoods
#   },
#   generate = {
#     # Calculation of posterior predictions and generated quantities
#   }
# )

## ----eval=FALSE---------------------------------------------------------------
# parameters = {
#   # Scalar parameter (with lower bound of 0)
#   sigma = Dim(lower = 0)
# 
#   # Vector parameter (length N)
#   mu = Dim(N)
# 
#   # Matrix parameter (N rows, M columns)
#   X = Dim(N, M)
# }

## ----eval=FALSE---------------------------------------------------------------
# model = {
#   # Priors
#   mu ~ normal(0, 10)
#   sigma ~ exponential(1)
# 
#   # Likelihood
#   Y ~ normal(mu, sigma)
# }

## ----eval=FALSE---------------------------------------------------------------
# model = {
#   lp <- lp + normal_lpdf(mu, 0, 10)
#   lp <- lp + exponential_lpdf(sigma, 1)
#   lp <- lp + normal_lpdf(Y, mu, sigma)
# }

## ----eval=FALSE---------------------------------------------------------------
# setup = {
#   N <- length(Y)  # Get the sample size of the observed data
#   P <- ncol(X)    # Get the number of columns in the design matrix (number of predictors)
# }