Rel 10.4.3 - Updates for cmdstan-2.33.0

StanJulia · Sep 15, 2023 · cf791df · cf791df
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diff --git a/Examples-Stan-Methods/Diagnose/diagnose.jl b/Examples-Stan-Methods/Diagnose/diagnose.jl
@@ -5,7 +5,7 @@ using StanDiagnose
 bernoulli_model = "
 data { 
   int<lower=0> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Stan-Methods/Generate_Quantities/generate_quantities.jl b/Examples-Stan-Methods/Generate_Quantities/generate_quantities.jl
@@ -5,7 +5,7 @@ ProjDir = @__DIR__
 gq = "
   data {
     int<lower=0> N;
-    int<lower=0> y[N];
+    array[N] int<lower=0> y;
   }
   parameters {
     real<lower=0.00001> Theta;

diff --git a/Examples-Stan-Methods/Optimize/optimize.jl b/Examples-Stan-Methods/Optimize/optimize.jl
@@ -5,7 +5,7 @@ using StanOptimize
 bernoulli_model = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Stan-Methods/Parse_and_Interpolate/parse.jl b/Examples-Stan-Methods/Parse_and_Interpolate/parse.jl
@@ -21,7 +21,7 @@ bernoulli_model = "
   }
   data { 
     int<lower=1> N; 
-    int<lower=0,upper=1> y[N];
+    array[N] int<lower=0,upper=1> y;
   } 
   parameters {
     real<lower=0,upper=1> theta;

diff --git a/Examples-Stan-Methods/StanQuap/howell1.jl b/Examples-Stan-Methods/StanQuap/howell1.jl
@@ -9,7 +9,7 @@ stan4_1 = "
 // Inferring the mean and std
 data {
   int N;
-  real<lower=0> h[N];
+  array[N] real<lower=0> h;
 }
 parameters {
   real<lower=0.1> sigma;

diff --git a/Examples-Test-Cases/Diagnostics/diagnostics.jl b/Examples-Test-Cases/Diagnostics/diagnostics.jl
@@ -5,7 +5,7 @@ using StanSample, MCMCChains
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Test-Cases/InitThetaDict/init_theta.jl b/Examples-Test-Cases/InitThetaDict/init_theta.jl
@@ -5,7 +5,7 @@ using StanSample, MCMCChains, Random
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Test-Cases/InitThetaDictArray/init_dict_array.jl b/Examples-Test-Cases/InitThetaDictArray/init_dict_array.jl
@@ -5,7 +5,7 @@ using StanSample, MCMCChains
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Test-Cases/InitThetaFile/init_file.jl b/Examples-Test-Cases/InitThetaFile/init_file.jl
@@ -7,7 +7,7 @@ ProjDir = @__DIR__
 bernoullimodel = "
 data { 
   int<lower=1> N;
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Test-Cases/NamedArray/namedarray.jl b/Examples-Test-Cases/NamedArray/namedarray.jl
@@ -5,7 +5,7 @@ using StanSample, MCMCChains
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Test-Cases/ScalarObs/scalar.jl b/Examples-Test-Cases/ScalarObs/scalar.jl
@@ -5,7 +5,7 @@ using StanSample, MCMCChains
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples-Test-Cases/ZeroLengthArray/zerolengtharray.jl b/Examples-Test-Cases/ZeroLengthArray/zerolengtharray.jl
@@ -3,8 +3,8 @@ using StanSample, MCMCChains
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
-  real empty[0];
+  array[N] int<lower=0,upper=1> y;
+  array[0] real empty;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples/Bernoulli/bernoulli.jl b/Examples/Bernoulli/bernoulli.jl
@@ -9,7 +9,7 @@ using StanSample
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/Examples/Dyes/dyes.jl b/Examples/Dyes/dyes.jl
@@ -15,14 +15,14 @@ parameters {
   real<lower=0> tau_between;
   real<lower=0> tau_within; 
   real theta;
-  real mu[BATCHES]; 
+  array[BATCHES] real mu; 
 } 
 
 transformed parameters {
   real sigma_between; 
   real sigma_within;
-  sigma_between <- 1/sqrt(tau_between); 
-  sigma_within <- 1/sqrt(tau_within); 
+  sigma_between = 1/sqrt(tau_between); 
+  sigma_within = 1/sqrt(tau_within); 
 } 
 
 model {
@@ -39,8 +39,8 @@ generated quantities {
   real sigmasq_between;
   real sigmasq_within;
 
-  sigmasq_between <- 1 / tau_between;
-  sigmasq_within <- 1 / tau_within;
+  sigmasq_between = 1 / tau_between;
+  sigmasq_within = 1 / tau_within;
 }
 "
 

diff --git a/Examples/Dyes/dyes_2.jl b/Examples/Dyes/dyes_2.jl
@@ -8,22 +8,21 @@ dyes ="
 data {
   int BATCHES; 
   int SAMPLES; 
-  real y[BATCHES, SAMPLES]; 
-  //vector[SAMPLES] y[BATCHES]; 
+  matrix[BATCHES, SAMPLES] y; 
 } 
 
 parameters {
   real<lower=0> tau_between;
   real<lower=0> tau_within; 
   real theta;
-  real mu[BATCHES]; 
+  array[BATCHES] real mu; 
 } 
 
 transformed parameters {
   real sigma_between; 
   real sigma_within;
-  sigma_between <- 1/sqrt(tau_between); 
-  sigma_within <- 1/sqrt(tau_within); 
+  sigma_between = 1/sqrt(tau_between); 
+  sigma_within = 1/sqrt(tau_within); 
 } 
 
 model {
@@ -40,8 +39,8 @@ generated quantities {
   real sigmasq_between;
   real sigmasq_within;
 
-  sigmasq_between <- 1 / tau_between;
-  sigmasq_within <- 1 / tau_within;
+  sigmasq_between = 1 / tau_between;
+  sigmasq_within = 1 / tau_within;
 }
 "
 

diff --git a/Examples/EightSchools/schools8.jl b/Examples/EightSchools/schools8.jl
@@ -5,18 +5,18 @@ using StanSample, MCMCChains
 eightschools ="
 data {
   int<lower=0> J; // number of schools
-  real y[J]; // estimated treatment effects
-  real<lower=0> sigma[J]; // s.e. of effect estimates
+  array[J] real y; // estimated treatment effects
+  array[J] real<lower=0> sigma; // s.e. of effect estimates
 }
 parameters {
   real mu;
   real<lower=0> tau;
-  real eta[J];
+  array[J] real eta;
 }
 transformed parameters {
-  real theta[J];
+  array[J] real theta;
   for (j in 1:J)
-    theta[j] <- mu + tau * eta[j];
+    theta[j] = mu + tau * eta[j];
 }
 model {
   eta ~ normal(0, 1);

diff --git a/Examples/RedCardsStudy/redcardstudy.jl b/Examples/RedCardsStudy/redcardstudy.jl
@@ -11,8 +11,8 @@ rcd[!, :rater1] = Float64.(rcd.rater1)
 stan_logistic_0 ="
 data {
   int<lower=0> N;
-  int<lower=0> n_redcards[N];
-  int<lower=0> n_games[N];
+  array[N] int<lower=0> n_redcards;
+  array[N] int<lower=0> n_games;
   vector[N] rating;
 }
 parameters {

diff --git a/Project.toml b/Project.toml
@@ -14,6 +14,7 @@ DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
+HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 InferenceObjects = "b5cf5a8d-e756-4ee3-b014-01d49d192c00"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"

diff --git a/test/Examples-Stan-Methods/Diagnose/diagnose.jl b/test/Examples-Stan-Methods/Diagnose/diagnose.jl
@@ -5,7 +5,7 @@ using StanDiagnose, Test
 bernoulli_model = "
 data { 
   int<lower=0> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples-Stan-Methods/Generate_Quantities/generate_quantities.jl b/test/Examples-Stan-Methods/Generate_Quantities/generate_quantities.jl
@@ -7,7 +7,7 @@ ProjDir = @__DIR__
 gq = "
   data {
     int<lower=0> N;
-    int<lower=0> y[N];
+    array[N] int<lower=0> y;
   }
   parameters {
     real<lower=0.00001> Theta;

diff --git a/test/Examples-Stan-Methods/Optimize/optimize.jl b/test/Examples-Stan-Methods/Optimize/optimize.jl
@@ -5,7 +5,7 @@ using StanOptimize, Test
 bernoulli_model = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples-Stan-Methods/Parse_and_Interpolate/parse.jl b/test/Examples-Stan-Methods/Parse_and_Interpolate/parse.jl
@@ -21,7 +21,7 @@ cd(ProjDir) do
     }
     data { 
       int<lower=1> N; 
-      int<lower=0,upper=1> y[N];
+      array[N] int<lower=0,upper=1> y;
     } 
     parameters {
       real<lower=0,upper=1> theta;

diff --git a/test/Examples-Stan-Methods/StanQuap/howell1.jl b/test/Examples-Stan-Methods/StanQuap/howell1.jl
@@ -9,7 +9,7 @@ stan4_1 = "
 // Inferring the mean and std
 data {
   int N;
-  real<lower=0> h[N];
+  array[N] real<lower=0> h;
 }
 parameters {
   real<lower=0.1> sigma;

diff --git a/test/Examples-Test-Cases/Diagnostics/diagnostics.jl b/test/Examples-Test-Cases/Diagnostics/diagnostics.jl
@@ -5,7 +5,7 @@ using StanSample, Test
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples-Test-Cases/InitThetaDict/init_dict.jl b/test/Examples-Test-Cases/InitThetaDict/init_dict.jl
@@ -5,7 +5,7 @@ using StanSample, Test
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples-Test-Cases/InitThetaDictArray/init_dict_array.jl b/test/Examples-Test-Cases/InitThetaDictArray/init_dict_array.jl
@@ -5,7 +5,7 @@ using StanSample, Test
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples-Test-Cases/InitThetaFile/init_file.jl b/test/Examples-Test-Cases/InitThetaFile/init_file.jl
@@ -5,7 +5,7 @@ using StanSample, Test
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples-Test-Cases/ScalarObs/scalar.jl b/test/Examples-Test-Cases/ScalarObs/scalar.jl
@@ -5,7 +5,7 @@ using StanSample, Test
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples-Test-Cases/ZeroLengthArray/zerolengtharray.jl b/test/Examples-Test-Cases/ZeroLengthArray/zerolengtharray.jl
@@ -3,8 +3,8 @@ using StanSample, Test
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
-  real empty[0];
+  array[N] int<lower=0,upper=1> y;
+  array[0] real empty;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples/Bernoulli/bernoulli.jl b/test/Examples/Bernoulli/bernoulli.jl
@@ -5,7 +5,7 @@ using StanSample, Test
 bernoullimodel = "
 data { 
   int<lower=1> N; 
-  int<lower=0,upper=1> y[N];
+  array[N] int<lower=0,upper=1> y;
 } 
 parameters {
   real<lower=0,upper=1> theta;

diff --git a/test/Examples/Binomial/binomial.jl b/test/Examples/Binomial/binomial.jl
@@ -22,8 +22,8 @@ generated quantities {
   int<lower=0> postpredk;
   int<lower=0> priorpredk;
 
-  postpredk <- binomial_rng(n, theta);
-  priorpredk <- binomial_rng(n, thetaprior);
+  postpredk = binomial_rng(n, theta);
+  priorpredk = binomial_rng(n, thetaprior);
 }
 "
 

diff --git a/test/Examples/Binormal/binormal.jl b/test/Examples/Binormal/binormal.jl
@@ -9,12 +9,12 @@ binorm_model = "
       matrix[2,2] Sigma;
       vector[2] mu;
 
-      mu[1] <- 0.0;
-      mu[2] <- 0.0;
-      Sigma[1,1] <- 1.0;
-      Sigma[2,2] <- 1.0;
-      Sigma[1,2] <- 0.10;
-      Sigma[2,1] <- 0.10;
+      mu[1] = 0.0;
+      mu[2] = 0.0;
+      Sigma[1,1] = 1.0;
+      Sigma[2,2] = 1.0;
+      Sigma[1,2] = 0.10;
+      Sigma[2,1] = 0.10;
   }
   parameters {
       vector[2] y;