Update deprecated syntax for future rstan compatibility

R/.gitignore

@@ -1,3 +1,4 @@
 hBayesDM*.tar.gz
 hBayesDM.Rcheck/
 docs/
+!inst/include/stan_meta_header.hpp

R/configure

@@ -0,0 +1,5 @@
+#! /bin/sh
+
+# Generated by rstantools.  Do not edit by hand.
+
+"${R_HOME}/bin/Rscript" -e "rstantools::rstan_config()"

R/configure.win

@@ -0,0 +1,5 @@
+#! /bin/sh
+
+# Generated by rstantools.  Do not edit by hand.
+
+"${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe" -e "rstantools::rstan_config()"

R/inst/include/stan_meta_header.hpp

@@ -0,0 +1 @@
+// Insert all #include<foo.hpp> statements here

commons/stan_files/alt_delta.stan

@@ -1,13 +1,13 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  int<lower=-1, upper=2> choice[N, T];
-  real outcome[N, T];  // no lower and upper bounds
-  real bluePunish[N, T];
-  real orangePunish[N, T];
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N, T] int<lower=-1, upper=2> choice;
+  array[N, T] real outcome;  // no lower and upper bounds
+  array[N, T] real bluePunish;
+  array[N, T] real orangePunish;
 }
 transformed data {
   real initV_ev;  // initial values for EV
@@ -88,10 +88,10 @@ generated quantities {
   real<lower=0, upper=10> mu_gamma;
 
   // For log likelihood calculation
-  real log_lik[N];
+  array[N] real log_lik;
 
   // For posterior predictive check
-  real y_pred[N, T];
+  array[N, T] real y_pred;
 
   // Set all posterior predictions to 0 (avoids NULL values)
   for (i in 1:N) {

commons/stan_files/alt_gamma.stan

@@ -1,13 +1,13 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  int<lower=1, upper=2> choice[N, T];
-  real outcome[N, T];  // no lower and upper bounds
-  real bluePunish[N, T];
-  real orangePunish[N, T];
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N, T] int<lower=1, upper=2> choice;
+  array[N, T] real outcome;  // no lower and upper bounds
+  array[N, T] real bluePunish;
+  array[N, T] real orangePunish;
 }
 transformed data {
   vector[2] initV_ev;  // initial values for EV
@@ -89,10 +89,10 @@ generated quantities {
   real<lower=0, upper=10> mu_gamma;
 
   // For log likelihood calculation
-  real log_lik[N];
+  array[N] real log_lik;
 
   // For posterior predictive check
-  real y_pred[N, T];
+  array[N, T] real y_pred;
 
   // Set all posterior predictions to 0 (avoids NULL values)
   for (i in 1:N) {

commons/stan_files/bandit2arm_delta.stan

@@ -1,11 +1,11 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  int<lower=-1, upper=2> choice[N, T];
-  real outcome[N, T];  // no lower and upper bounds
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N, T] int<lower=-1, upper=2> choice;
+  array[N, T] real outcome;  // no lower and upper bounds
 }
 transformed data {
   vector[2] initV;  // initial values for EV
@@ -65,10 +65,10 @@ generated quantities {
   real<lower=0, upper=5> mu_tau;
 
   // For log likelihood calculation
-  real log_lik[N];
+  array[N] real log_lik;
 
   // For posterior predictive check
-  real y_pred[N, T];
+  array[N, T] real y_pred;
 
   // Set all posterior predictions to 0 (avoids NULL values)
   for (i in 1:N) {

commons/stan_files/bandit4arm2_kalman_filter.stan

@@ -1,11 +1,11 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  int<lower=1,upper=4> choice[N,T];
-  real<lower=1,upper=100> outcome[N,T];
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N,T] int<lower=1,upper=4> choice;
+  array[N,T] real<lower=1,upper=100> outcome;
 }
 
 transformed data {
@@ -104,8 +104,8 @@ generated quantities {
   real<lower=0,upper=100> mu_mu0;
   real<lower=0,upper=15> mu_s0;
   real<lower=0,upper=15> mu_sD;
-  real log_lik[N];
-  real y_pred[N,T];
+  array[N] real log_lik;
+  array[N,T] real y_pred;
 
   for (i in 1:N) {
     for (t in 1:T) {

commons/stan_files/bandit4arm_2par_lapse.stan

@@ -1,4 +1,4 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 // Seymour et al 2012 J neuro model, w/o C (chioce perseveration) but with xi (lapse rate)
 // w/o reward sensitivity and punishment sensitivity
@@ -7,10 +7,10 @@
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  real rew[N, T];
-  real los[N, T];
-  int choice[N, T];
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N, T] real rew;
+  array[N, T] real los;
+  array[N, T] int choice;
 }
 
 transformed data {
@@ -104,10 +104,10 @@ generated quantities {
   real<lower=0, upper=1> mu_xi;
 
   // For log likelihood calculation
-  real log_lik[N];
+  array[N] real log_lik;
 
   // For posterior predictive check
-  real y_pred[N, T];
+  array[N, T] real y_pred;
 
   // Set all posterior predictions to 0 (avoids NULL values)
   for (i in 1:N) {

commons/stan_files/bandit4arm_4par.stan

@@ -1,13 +1,13 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 // Seymour et al 2012 J neuro model, w/o C (chioce perseveration)
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  real rew[N, T];
-  real los[N, T];
-  int choice[N, T];
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N, T] real rew;
+  array[N, T] real los;
+  array[N, T] int choice;
 }
 
 transformed data {
@@ -106,10 +106,10 @@ generated quantities {
   real<lower=0, upper=30> mu_P;
 
   // For log likelihood calculation
-  real log_lik[N];
+  array[N] real log_lik;
 
   // For posterior predictive check
-  real y_pred[N, T];
+  array[N, T] real y_pred;
 
   // Set all posterior predictions to 0 (avoids NULL values)
   for (i in 1:N) {

commons/stan_files/bandit4arm_lapse.stan

@@ -1,13 +1,13 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 // Seymour et al 2012 J neuro model, w/o C (chioce perseveration) but with xi (lapse rate)
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  real rew[N, T];
-  real los[N, T];
-  int choice[N, T];
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N, T] real rew;
+  array[N, T] real los;
+  array[N, T] int choice;
 }
 
 transformed data {
@@ -111,10 +111,10 @@ generated quantities {
   real<lower=0, upper=1> mu_xi;
 
   // For log likelihood calculation
-  real log_lik[N];
+  array[N] real log_lik;
 
   // For posterior predictive check
-  real y_pred[N, T];
+  array[N, T] real y_pred;
 
   // Set all posterior predictions to 0 (avoids NULL values)
   for (i in 1:N) {

commons/stan_files/bandit4arm_lapse_decay.stan

@@ -1,14 +1,14 @@
-#include /pre/license.stan
+#include /include/license.stan
 
 // Seymour et al 2012 J neuro model, w/o C (chioce perseveration) but with xi (lapse rate). Added decay rate (Niv et al., 2015, J. Neuro)
 // Aylward et al., 2018, PsyArXiv
 data {
   int<lower=1> N;
   int<lower=1> T;
-  int<lower=1, upper=T> Tsubj[N];
-  real rew[N, T];
-  real los[N, T];
-  int choice[N, T];
+  array[N] int<lower=1, upper=T> Tsubj;
+  array[N, T] real rew;
+  array[N, T] real los;
+  array[N, T] int choice;
 }
 
 transformed data {
@@ -123,10 +123,10 @@ generated quantities {
   real<lower=0, upper=1> mu_d;
 
   // For log likelihood calculation
-  real log_lik[N];
+  array[N] real log_lik;
 
   // For posterior predictive check
-  real y_pred[N, T];
+  array[N, T] real y_pred;
 
   // Set all posterior predictions to 0 (avoids NULL values)
   for (i in 1:N) {