data {
  int<lower=0> N;
  real y[N];
  matrix[N,N] A;
  #vector[N] mu_u_prior;
}
transformed data {
  matrix[N,N] t_LA_inv;
  matrix[N,N] A_inv; A_inv <- inverse(A);
  t_LA_inv <- transpose(cholesky(A_inv));
}
parameters{
 real beta0;
 vector[N] u;	
 real<lower=0> sigmasq_e;
 real<lower=0> sigmasq_u;	
} 
model {
  ##-----------------------------------------------
  vector[N] v;
  real sum_of_squares;
  v <- t_LA_inv * u;
  sum_of_squares <- 0.0; // or sum(pow(v, 2)) may / should work
  for(i in 1:N) {
    sum_of_squares <- sum_of_squares + pow(v[i], 2);
  }
  lp__ <- lp__ - 0.5 * N * log(sigmasq_u); // non-constant part of log(det(sigmasq_u * A)^-0.5)
  lp__ <- lp__ - sum_of_squares / (2 * sigmasq_u); // log of kernel of multivariate normal 
  ## likelihood	
  for(i in 1:N){
       y[i] ~ normal(beta0 + u[i], sqrt(sigmasq_e)); 
     }  
  ##---------------------------------
  ## priors
  beta0 ~ normal(0,1000);
  sigmasq_e ~ scaled_inv_chi_square(2,0.25);
  sigmasq_u ~ scaled_inv_chi_square(2,0.25);	
}

## TRANSLATING MODEL 'anon_model' FROM Stan CODE TO C++ CODE NOW.
## Error in stanc(file = file, model_code = model_code, model_name = model_name,  : 
##   failed to parse Stan model 'anon_model' and error message provided as:
## LOCATION:  file=input; line=11, column=40

##   t_LA_inv <- transpose(cholesky(A_inv));
##                                        ^-- here

## DIAGNOSTIC(S) FROM PARSER:

## no matches for function name="cholesky"
##     arg 0 type=matrix
## expression is ill formed
## Parser expecting: <eps>