Some fixes, comment out half of the stuff

Author: penelopeysm

_quarto.yml

@@ -50,40 +50,40 @@ website:
     - text: documentation
       collapse-level: 1
       contents:
-        - getting-started/index.qmd
+        # - getting-started/index.qmd
         - core-functionality/index.qmd
 
         - section: "User Guide"
           collapse-level: 1
           contents:
-            - usage/automatic-differentiation/index.qmd
-            - usage/custom-distribution/index.qmd
-            - usage/probability-interface/index.qmd
-            - usage/modifying-logprob/index.qmd
-            - usage/generated-quantities/index.qmd
-            - usage/mode-estimation/index.qmd
-            - usage/performance-tips/index.qmd
-            - usage/sampler-visualisation/index.qmd
-            - usage/dynamichmc/index.qmd
-            - usage/external-samplers/index.qmd
+            # - usage/automatic-differentiation/index.qmd
+            # - usage/custom-distribution/index.qmd
+            # - usage/probability-interface/index.qmd
+            # - usage/modifying-logprob/index.qmd
+            # - usage/generated-quantities/index.qmd
+            # - usage/mode-estimation/index.qmd
+            # - usage/performance-tips/index.qmd
+            # - usage/sampler-visualisation/index.qmd
+            # - usage/dynamichmc/index.qmd
+            # - usage/external-samplers/index.qmd
 
         - section: "Tutorials"
           contents:
             - tutorials/coin-flipping/index.qmd
             - tutorials/gaussian-mixture-models/index.qmd
-            - tutorials/bayesian-logistic-regression/index.qmd
-            - tutorials/bayesian-neural-networks/index.qmd
+            # - tutorials/bayesian-logistic-regression/index.qmd
+            # - tutorials/bayesian-neural-networks/index.qmd
             - tutorials/hidden-markov-models/index.qmd
-            - tutorials/bayesian-linear-regression/index.qmd
+            # - tutorials/bayesian-linear-regression/index.qmd
             - tutorials/infinite-mixture-models/index.qmd
-            - tutorials/bayesian-poisson-regression/index.qmd
-            - tutorials/multinomial-logistic-regression/index.qmd
+            # - tutorials/bayesian-poisson-regression/index.qmd
+            # - tutorials/multinomial-logistic-regression/index.qmd
             - tutorials/variational-inference/index.qmd
-            - tutorials/bayesian-differential-equations/index.qmd
-            - tutorials/probabilistic-pca/index.qmd
-            - tutorials/bayesian-time-series-analysis/index.qmd
+            # - tutorials/bayesian-differential-equations/index.qmd
+            # - tutorials/probabilistic-pca/index.qmd
+            # - tutorials/bayesian-time-series-analysis/index.qmd
             - tutorials/gaussian-processes-introduction/index.qmd
-            - tutorials/gaussian-process-latent-variable-models/index.qmd
+            # - tutorials/gaussian-process-latent-variable-models/index.qmd
 
         - section: "Developers"
           contents:

tutorials/infinite-mixture-models/index.qmd

@@ -188,10 +188,10 @@ In Turing we can implement an infinite Gaussian mixture model using the Chinese
     H = Normal(μ0, σ0)
 
     # Latent assignment.
-    z = tzeros(Int, length(x))
+    z = zeros(Int, length(x))
 
     # Locations of the infinitely many clusters.
-    μ = tzeros(Float64, 0)
+    μ = zeros(Float64, 0)
 
     for i in 1:length(x)