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Update deprecated Stan array syntax #23

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Update deprecated Stan array syntax #23

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32 changes: 16 additions & 16 deletions inst/stan/negBinomial_MPI.stan
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Original file line number Diff line number Diff line change
Expand Up @@ -29,10 +29,10 @@ functions{
return log(gamma_rng(a, b));
}

vector[] get_reference_parameters_MPI(int n_shards, int M, int[] G_per_shard, int[,] G_ind, matrix lambda_log, vector sigma, vector exposure_rate){
array[] vector get_reference_parameters_MPI(int n_shards, int M, array[] int G_per_shard, array[,] int G_ind, matrix lambda_log, vector sigma, vector exposure_rate){

int S = rows(exposure_rate);
vector[(M*S) + M + S] lambda_sigma_exposure_MPI[n_shards];
array[n_shards] vector[(M*S) + M + S] lambda_sigma_exposure_MPI;

for( i in 1:n_shards ) {

Expand All @@ -55,7 +55,7 @@ functions{
return(lambda_sigma_exposure_MPI);
}

vector lp_reduce( vector global_parameters , vector local_parameters , real[] real_data , int[] int_data ) {
vector lp_reduce( vector global_parameters , vector local_parameters , array[] real real_data , array[] int int_data ) {

real lp;

Expand All @@ -64,13 +64,13 @@ functions{
int N = int_data[2];
int S = int_data[3];
int G_per_shard = int_data[4];
int symbol_end[M+1] = int_data[(4+1):(4+1+M)];
int sample_idx[N] = int_data[(4+1+M+1):(4+1+M+1+N-1)];
int counts[N] = int_data[(4+1+M+1+N-1+1):(4+1+M+1+N-1+N)];
array[M+1] int symbol_end = int_data[(4+1):(4+1+M)];
array[N] int sample_idx = int_data[(4+1+M+1):(4+1+M+1+N-1)];
array[N] int counts = int_data[(4+1+M+1+N-1+1):(4+1+M+1+N-1+N)];

// Data to exclude for outliers
int size_exclude = int_data[(4+1+M+1+N-1+N+1)];
int to_exclude[size_exclude] = int_data[(4+1+M+1+N-1+N+1+1):(4+1+M+1+N-1+N+1+size_exclude)]; // we are lucky for packaging it is the last variabe
array[size_exclude] int to_exclude = int_data[(4+1+M+1+N-1+N+1+1):(4+1+M+1+N-1+N+1+size_exclude)]; // we are lucky for packaging it is the last variabe

// Parameters unpack
vector[G_per_shard*S] lambda_MPI = local_parameters[1:(G_per_shard*S)];
Expand Down Expand Up @@ -147,15 +147,15 @@ data {
int<lower=0> G;
int<lower=0> S;
int n_shards;
int<lower=0> counts[n_shards, N];
int<lower=0> symbol_end[n_shards, M+1];
int<lower=0> G_ind[n_shards, M];
int<lower=0> sample_idx[n_shards, N];
int<lower=0> G_per_shard[n_shards];
int<lower=0> G_per_shard_idx[n_shards + 1];
array[n_shards, N] int<lower=0> counts;
array[n_shards, M+1] int<lower=0> symbol_end;
array[n_shards, M] int<lower=0> G_ind;
array[n_shards, N] int<lower=0> sample_idx;
array[n_shards] int<lower=0> G_per_shard;
array[n_shards + 1] int<lower=0> G_per_shard_idx;

int<lower=0> CP; // Counts package size
int<lower=0> counts_package[n_shards, CP];
array[n_shards, CP] int<lower=0> counts_package;

int<lower=1> C; // Covariates
matrix[S,C] X; // Design matrix
Expand All @@ -174,7 +174,7 @@ data {
transformed data {

vector[0] global_parameters;
real real_data[n_shards, 0];
array[n_shards, 0] real real_data;

}
parameters {
Expand Down Expand Up @@ -257,7 +257,7 @@ model {

}
generated quantities{
vector[how_many_to_check] counts_rng[S];
array[S] vector[how_many_to_check] counts_rng;

for(g in 1:how_many_to_check) for(s in 1:S)
// Make the overdispersion bigger making sigma smaller. Because inferring on truncated data with naive NB underestimate overdispersion
Expand Down