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Update deprecated syntax for future rstan compatibility #20

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Sep 11, 2023
Merged

Update deprecated syntax for future rstan compatibility #20

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6 changes: 3 additions & 3 deletions DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -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,
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2 changes: 1 addition & 1 deletion inst/stan/rw1_model.stan
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Expand Up @@ -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;
}

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2 changes: 1 addition & 1 deletion inst/stan/rw1_model_naive.stan
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Expand Up @@ -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;
}
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20 changes: 10 additions & 10 deletions inst/stan/walker_glm.stan
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Expand Up @@ -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);
Expand Down Expand Up @@ -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);
Expand Down Expand Up @@ -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;
Expand All @@ -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;
Expand All @@ -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));
Expand Down Expand Up @@ -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 {
Expand Down Expand Up @@ -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
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12 changes: 6 additions & 6 deletions inst/stan/walker_lm.stan
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Expand Up @@ -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);
Expand Down Expand Up @@ -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);
Expand Down Expand Up @@ -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;

Expand All @@ -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));
Expand Down Expand Up @@ -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;
}

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