bru(components = ~Intercept(1), ..., options = list())

bru_rerun(result, options = list())

if (bru_safe_inla(multicore = FALSE)) {

  # Simulate some covariates x and observations y
  input.df <- data.frame(x = cos(1:10))
  input.df <- within(input.df, y <- 5 + 2 * x + rnorm(10, mean = 0, sd = 0.1))

  # Fit a Gaussian likelihood model
  fit <- bru(y ~ x + Intercept, family = "gaussian", data = input.df)

  # Obtain summary
  fit$summary.fixed
}


if (bru_safe_inla(multicore = FALSE)) {

  # Alternatively, we can use the like() function to construct the likelihood:

  lik <- like(family = "gaussian", formula = y ~ x + Intercept, data = input.df)
  fit <- bru(~ x + Intercept(1), lik)
  fit$summary.fixed
}

# An important addition to the INLA methodology is bru's ability to use
# non-linear predictors. Such a predictor can be formulated via like()'s
# \code{formula} parameter. The z(1) notation is needed to ensure that
# the z component should be interpreted as single latent variable and not
# a covariate:

if (bru_safe_inla(multicore = FALSE)) {
  z <- 2
  input.df <- within(input.df, y <- 5 + exp(z) * x + rnorm(10, mean = 0, sd = 0.1))
  lik <- like(
    family = "gaussian", data = input.df,
    formula = y ~ exp(z) * x + Intercept
  )
  fit <- bru(~ z(1) + Intercept(1), lik)

  # Check the result (z posterior should be around 2)
  fit$summary.fixed
}