Merge pull request #20 from andrjohns/array-syntax

Update deprecated syntax for future rstan compatibility

DESCRIPTION

@@ -26,7 +26,7 @@ Depends:
  bayesplot,
  R (>= 3.4.0),
  Rcpp (>= 0.12.9),
- rstan (>= 2.18.1)
+ rstan (>= 2.26.0)
 Imports:
  coda,
  dplyr,
@@ -39,8 +39,8 @@ Imports:
  rlang,
  rstantools (>= 2.0.0)
 LinkingTo: 
- StanHeaders (>= 2.18.0), 
- rstan (>= 2.18.1), 
+ StanHeaders (>= 2.26.0), 
+ rstan (>= 2.26.0), 
  BH (>= 1.66.0),
  Rcpp (>= 0.12.9), 
  RcppArmadillo, 

inst/stan/rw1_model.stan

@@ -80,7 +80,7 @@ transformed data {
 }
 
 parameters {
- real<lower=0> sigma_b[k];
+ array[k] real<lower=0> sigma_b;
  real<lower=0> sigma_y;
 }
 

inst/stan/rw1_model_naive.stan

@@ -17,7 +17,7 @@ transformed data {
 }
 
 parameters {
- real<lower=0> sigma_b[k];
+ array[k] real<lower=0> sigma_b;
  real<lower=0> sigma_y;
  matrix[k, n] beta_raw;
 }

inst/stan/walker_glm.stan

@@ -11,8 +11,8 @@ functions {
  // univariate Kalman filter & smoother for non-gaussian model, 
  // returns the log-likelihood of the corresponding approximating Gaussian model
  // and a extra correction term
- matrix glm_approx_loglik(vector y, int[] y_miss, vector a1, matrix P1, vector Ht, 
- matrix Tt, matrix Rt, matrix xreg, int distribution, int[] u, 
+ matrix glm_approx_loglik(vector y, array[] int y_miss, vector a1, matrix P1, vector Ht, 
+ matrix Tt, matrix Rt, matrix xreg, int distribution, array[] int u, 
  vector y_original, vector xbeta_fixed) {
 
  int k = rows(xreg);
@@ -99,7 +99,7 @@ functions {
  // univariate Kalman filter & smoother for non-gaussian model, 
  // returns the log-likelihood of the corresponding approximating Gaussian model
  // and a extra correction term
- matrix glm_approx_smoother(vector y, int[] y_miss, vector a1, matrix P1, vector Ht, 
+ matrix glm_approx_smoother(vector y, array[] int y_miss, vector a1, matrix P1, vector Ht, 
  matrix Tt, matrix Rt, matrix xreg) {
 
  int k = rows(xreg);
@@ -171,7 +171,7 @@ data {
  matrix[n, k_fixed] xreg_fixed;
  matrix[k, n] xreg_rw;
  vector[n] y;
- int<lower=0> y_miss[n];
+ array[n] int<lower=0> y_miss;
 
  real beta_fixed_mean;
  real<lower=0> beta_fixed_sd;
@@ -190,7 +190,7 @@ data {
 
  vector[n] Ht;
  vector[n] y_original;
- int<lower=0> u[n];
+ array[n] int<lower=0> u;
  int distribution;
  int<lower=0> N;
  matrix[k_rw1, n] gamma_rw1;
@@ -199,7 +199,7 @@ data {
 
 transformed data {
  // indexing for non-missing observations for log_lik
- int obs_idx[n_lfo - sum(y_miss[1:n_lfo])];
+ array[n_lfo - sum(y_miss[1:n_lfo])] int obs_idx;
  vector[m] a1;
  matrix[m, m] P1 = rep_matrix(0.0, m, m);
  matrix[m, m] Tt = diag_matrix(rep_vector(1.0, m));
@@ -233,8 +233,8 @@ transformed data {
 
 parameters {
  vector[k_fixed] beta_fixed;
- real<lower=0> sigma_rw1[k_rw1];
- real<lower=0> sigma_rw2[k_rw2];
+ array[k_rw1] real<lower=0> sigma_rw1;
+ array[k_rw2] real<lower=0> sigma_rw2;
 }
 
 transformed parameters {
@@ -290,8 +290,8 @@ generated quantities{
  vector[n] y_rep_j;
  matrix[k, n] beta_j;
  matrix[k_rw2, n] nu_j;
- real beta_array[k, n, N];
- real nu_array[k_rw2, n, N];
+ array[k, n, N] real beta_array;
+ array[k_rw2, n, N] real nu_array;
  vector[N] w = rep_vector(0.0, N); //importance sampling weights
 
  // This is the simplest but not the most efficient way to sample multiple realizations

inst/stan/walker_lm.stan

@@ -3,7 +3,7 @@ functions {
  // note that these functions are not fully optimised yet
 
  // univariate Kalman filter for RW1+RW2 model, returns the log-likelihood
- vector gaussian_filter(vector y, int[] y_miss, vector a1, matrix P1, real Ht, 
+ vector gaussian_filter(vector y, array[] int y_miss, vector a1, matrix P1, real Ht, 
  matrix Tt, matrix Rt, matrix xreg, vector gamma2_y) {
 
  int k = rows(xreg);
@@ -40,7 +40,7 @@ functions {
 
  }
 
- matrix gaussian_smoother(vector y, int[] y_miss, vector a1, matrix P1, real Ht, 
+ matrix gaussian_smoother(vector y, array[] int y_miss, vector a1, matrix P1, real Ht, 
  matrix Tt, matrix Rt, matrix xreg,vector gamma2_y) {
 
  int k = rows(xreg);
@@ -111,7 +111,7 @@ data {
  matrix[n, k_fixed] xreg_fixed;
  matrix[k, n] xreg_rw;
  vector[n] y;
- int<lower=0> y_miss[n];
+ array[n] int<lower=0> y_miss;
  real<lower=0> sigma_y_shape;
  real<lower=0> sigma_y_rate;
 
@@ -136,7 +136,7 @@ data {
 
 transformed data {
  // indexing for non-missing observations for log_lik
- int obs_idx[n_lfo - sum(y_miss[1:n_lfo])];
+ array[n_lfo - sum(y_miss[1:n_lfo])] int obs_idx;
  vector[m] a1;
  matrix[m, m] P1 = rep_matrix(0.0, m, m);
  matrix[m, m] Tt = diag_matrix(rep_vector(1.0, m));
@@ -170,8 +170,8 @@ transformed data {
 
 parameters {
  vector[k_fixed] beta_fixed;
- real<lower=0> sigma_rw1[k_rw1];
- real<lower=0> sigma_rw2[k_rw2];
+ array[k_rw1] real<lower=0> sigma_rw1;
+ array[k_rw2] real<lower=0> sigma_rw2;
  real<lower=0> sigma_y;
 }