Merge branch 'master' of https://github.com/stan-dev/example-models

Author: mitzimorris

BPA/Ch.10/js_ms.stan

@@ -121,6 +121,7 @@ generated quantities {
   array[M, n_occasions] int<lower=1, upper=3> z; // Latent state
 
   // Generate z[]
+  // This code generates the posterior predictive distribution
   for (i in 1 : M) {
     z[i, 1] = 1;
     for (t in 2 : n_occasions) {

BPA/Ch.10/js_rest_occ.stan

@@ -212,6 +212,7 @@ generated quantities {
   array[M, n_occasions] int z; // Latent state
 
   // Generate z[]
+  // This code generates the posterior predictive distribution
   for (i in 1 : M) {
     int q = 1;
     real mu2;

BPA/Ch.10/js_super.stan

@@ -215,6 +215,7 @@ generated quantities {
   array[M, n_occasions] int<lower=0, upper=1> z; // Deflated latent state
 
   // Generate w[] and z[]
+  // This code generates the posterior predictive distribution
   for (i in 1 : M) {
     int q = 1;
     if (bernoulli_rng(psi)) {

BPA/Ch.10/js_super_indran.stan

@@ -226,6 +226,7 @@ generated quantities {
   sigma2 = square(sigma);
 
   // Generate w[] and z[]
+  // This code generates the posterior predictive distribution
   for (i in 1 : M) {
     int q = 1;
     if (bernoulli_rng(psi)) {

BPA/Ch.10/js_tempran.stan

@@ -223,6 +223,7 @@ generated quantities {
   array[M, n_occasions] int z; // Latent state
 
   // Generate z[]
+  // This code generates the posterior predictive distribution
   for (i in 1 : M) {
     int q = 1;
     real mu2;

jupyter/radon/LICENSE

@@ -0,0 +1,6 @@
+Notebook based on Gelman and Hill 2007, Radon case study.
+Introduces hierarchical models and Python packages `cmdstanpy`, and `plotnine`.
+
+Author:  Mitzi Morris
+
+

jupyter/radon/README.md

@@ -0,0 +1,18 @@
+Case study and jupyter notebook which demonstrate the correctness and efficiency of the
+`sum_to_zero_vector` constrained parameter type, introduced in Stan 2.36.
+
+
+- Case study `sum_to_zero_evaluation.qmd` demonstrates use of the `sum_to_zero_vector` for 2 models.
+
+- Jupyter notebook `sum_to_zero_evalutation.ipynb` is a step-by-step explanation of the operations
+used to carry out this evaluation. 
+
+Included in the GitHub repository for this notebook are several python files of helper functions.
+
+* eval_efficiencies.py - run models repeatedly, get average performance stats.
+* utils.py - simulate data for binomial model.
+* utils\_html.py - format Stan summaries for this notebook.
+* utils\_bym2.py - compute data inputs to the BYM2 model.
+* utils\_nyc\_map.py - munge the New York City census tract map.
+
+Author:  Mitzi Morris

jupyter/sum-to-zero/LICENSE

@@ -0,0 +1 @@
+{"runtime, ave":{"ozs large":2.41,"hard large":3.25,"soft large":17.41},"runtime, std dev":{"ozs large":0.08,"hard large":0.09,"soft large":0.5},"ESS_bulk\/s":{"ozs large":1305.86,"hard large":981.53,"soft large":181.15}}