diff --git a/.gitignore b/.gitignore
index 31e7331..dd718b2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,5 +52,7 @@ rsconnect/
 
 # For niimble branch
 _site/
+*_files/
+*_cache/
 01_nimble.html
 local_nimble*.R
diff --git a/01_nimble.qmd b/01_nimble.qmd
index b85f18f..9f3e74f 100644
--- a/01_nimble.qmd
+++ b/01_nimble.qmd
@@ -40,13 +40,13 @@ But, this remains limited and to have full flexibility, ones need to build its o
 
 ```{r}
 #| label: example1_data
-
-data_ex1 <- rnbinom(n = 10, prob = 0.4, size = 12)
+data_ex1 <- rnbinom(n = 1000, prob = 0.4, size = 12)
 ```
 
 
 
 ```{r}
+#| label: code_neg_bin
 code_neg_bin <- nimbleCode({
   # Observation model
   for(i in 1:n){# n is never defined before, it will be a constant
@@ -77,11 +77,125 @@ model_neg_bin <- nimbleModel(code = code_neg_bin,
 ```{r}
 #| label: posterior_samples
 #| cache: true
-posterior_samples_neg_bin <- nimbleMCMC(model_neg_bin)
+posterior_samples_neg_bin <- nimbleMCMC(model_neg_bin, 
+                                        nchains = 3, 
+                                        niter = 10000, 
+                                        thin = 10, 
+                                        nburnin = 1000)
 ```
 
 ## Exploring the results
 
+```{r}
+#| label: str_posterior_samples
+str(posterior_samples_neg_bin)
+```
+
+```{r}
+#| label: custom_formatted_results
+formatted_results <- imap_dfr(posterior_samples_neg_bin, 
+     function(x, nm){
+       as.data.frame(x) %>% 
+         rowid_to_column(var = "Iteration") %>% 
+         mutate(Chain = str_remove(nm, "chain"))
+     }) %>% 
+  pivot_longer(cols = -c("Iteration", "Chain"),
+               names_to = "Parameter", 
+               values_to = "value")
+```
+
+```{r}
+#| label: plot
+ggplot(formatted_results) +
+  aes(x = Iteration,
+      y = value, color = Chain) +
+  facet_wrap(~Parameter, scales = "free") +
+  geom_line() +
+  labs(x = "Sample ID", y = "Parameter value", color = "")
+```
+
+## Package for automatic formatting
+
+```{r}
+#| label: posterior_samples_coda
+#| cache: true
+posterior_samples_neg_bin <- nimbleMCMC(model_neg_bin, 
+                                        nchains = 3, 
+                                        niter = 10000, 
+                                        thin = 10, 
+                                        nburnin = 1000,
+                                        samplesAsCodaMCMC = TRUE)
+```
+
+```{r}
+#| label: str_posterior_samples_coda
+str(posterior_samples_neg_bin)
+```
+
+```{r}
+#| label: coda_formatted_results
+formatted_results <- ggs(posterior_samples_neg_bin)
+formatted_results
+```
+
+## Defining a `nimbleFunction`
+
+```{r}
+get_prob <- nimbleFunction(
+    run = function(log_mu = double(0),
+                   log_theta = double(0)) { # type declarations
+        z <- log_theta - log_mu
+        output <- 1 / (1 + exp(-z))
+        return(output)
+        returnType(double(0))  # return type declaration
+    })
+get_prob(log(18), log(12))
+```
+
+
+```{r}
+#| label: code_neg_bin_alternatif
+#| cache: true
+code_alternatif <- nimbleCode({
+  # Observation model
+  for(i in 1:n){# n is never defined before, it will be a constant
+    y[i] ~ dnbinom(prob, theta)
+  }
+  # Alternative vectorized formulation 
+  # y[1:n] ~ dnbinom(prob, theta)
+  # PRIORS
+  log_mu ~ dnorm(0, 1)
+  theta ~ dexp(0.1)
+  # Quantites deterministes
+  log_theta <- log(theta)
+  prob <- get_prob(log_mu = log_mu, log_theta = log_theta)
+})
+
+model_alternatif <- nimbleModel(code = code_alternatif, 
+                    name = "Alternative negative binomial", 
+                    constants = list(n = length(data_ex1)),
+                    data = list(y = data_ex1),
+                    inits = list(mu = 0.5, theta = 1))
+
+posterior_samples_alternatif <- nimbleMCMC(model_alternatif, 
+                                        nchains = 3, 
+                                        niter = 10000, 
+                                        thin = 10, 
+                                        nburnin = 1000,
+                                        monitors = c("prob", "theta"),
+                                        samplesAsCodaMCMC = TRUE)
+```
+
+```{r}
+posterior_samples_alternatif %>% 
+  ggs() %>% 
+  ggplot() +
+  aes(x = Iteration,
+      y = value, color = factor(Chain)) +
+  facet_wrap(~Parameter, scales = "free") +
+  geom_line() +
+  labs(x = "Sample ID", y = "Parameter value", color = "")
+```
 
 
 ## Alternative MCMC sampler