takes no arguments and returns a character vector of parameter names

takes a numeric vector of parameters and returns the log of the unnormalized density with constants but without a Jacobian correction

takes a numeric vector of parameters and returns the log of the unnormalized density with constants and a Jacobian correction

takes a numeric vector of parameters and returns the log of the unnormalized density without constants and without a Jacobian correction

takes a numeric vector of parameters and returns the log of the unnormalized density without constants but with a Jacobian correction

std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > model_name()

takes no arguments and returns the MD5 hashed model

takes a list of constrained parameters and returns a numeric vector of unconstrained parameters

takes flags for include_tparams and include_gqs and returns a character vector of names of unknown quantities in the unconstrained space

std::vector<double, std::allocator<double> > write_array(std::vector<double, std::allocator<double> >, bool, bool, unsigned int, unsigned int)

takes a vector of unconstrained parameters, flags for include_tparams and include_gqs, as well as integers for id and seed and returns a vector of constrained parameters

Rcpp::List write_list(std::vector<double, std::allocator<double> >, bool, bool, unsigned int, unsigned int)

takes a vector of unconstrained parameters, flags for include_tparams and include_gqs, as well as integers for id and seed and returns a list of constrained parameters with the appropriate dimensions

// generated with brms 2.15.6
functions {
}
data {
  int<lower=1> N;  // total number of observations
  int<lower=1> N_y;  // number of observations
  vector[N_y] Y_y;  // response variable
  int<lower=1> Ksp_y;  // number of special effects terms
  int<lower=1> N_x1;  // number of observations
  vector[N_x1] Y_x1;  // response variable
  // data for measurement-error in the response
  vector<lower=0>[N_x1] noise_x1;
  // information about non-missings
  int<lower=0> Nme_x1;
  int<lower=1> Jme_x1[Nme_x1];
  int prior_only;  // should the likelihood be ignored?
}
transformed data {
}
parameters {
  real Intercept_y;  // temporary intercept for centered predictors
  vector[Ksp_y] bsp_y;  // special effects coefficients
  real<lower=0> sigma_y;  // dispersion parameter
  vector[N_x1] Yl_x1;  // latent variable
  real<lower=0> sigma_x1;  // dispersion parameter
}
transformed parameters {
}
model {
  // likelihood including constants
  if (!prior_only) {
    // initialize linear predictor term
    vector[N_y] mu_y = Intercept_y + rep_vector(0.0, N_y);
    // initialize linear predictor term
    vector[N_x1] mu_x1 = rep_vector(0.0, N_x1);
    for (n in 1:N_y) {
      // add more terms to the linear predictor
      mu_y[n] += (bsp_y[1]) * Yl_x1[n];
    }
    target += normal_lpdf(Y_y | mu_y, sigma_y);
    target += normal_lpdf(Yl_x1 | mu_x1, sigma_x1);
  }
  // priors including constants
  target += student_t_lpdf(Intercept_y | 3, 0.1, 2.5);
  target += student_t_lpdf(sigma_y | 3, 0, 2.5)
    - 1 * student_t_lccdf(0 | 3, 0, 2.5);
  target += normal_lpdf(Y_x1[Jme_x1] | Yl_x1[Jme_x1], noise_x1[Jme_x1]);
  target += student_t_lpdf(sigma_x1 | 3, 0, 2.5)
    - 1 * student_t_lccdf(0 | 3, 0, 2.5);
}
generated quantities {
  // actual population-level intercept
  real b_y_Intercept = Intercept_y;
}