diff --git a/.github/workflows/R-CMD-check-rstan.yaml b/.github/workflows/R-CMD-check-rstan.yaml
index c1674136..1e68bbe1 100644
--- a/.github/workflows/R-CMD-check-rstan.yaml
+++ b/.github/workflows/R-CMD-check-rstan.yaml
@@ -23,8 +23,10 @@ jobs:
           - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest, r: 'release'}
 
+# Use a CRAN-like environment to emulate CRAN submission checks
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      NOT_CRAN: false
       R_KEEP_PKG_SOURCE: yes
 
     steps:
diff --git a/R/mvgam.R b/R/mvgam.R
index 20303ae8..8b119e2f 100644
--- a/R/mvgam.R
+++ b/R/mvgam.R
@@ -396,13 +396,13 @@
 #'
 #' # Fit the model using AR1 trends
 #' mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
-#'               trend_map = trend_map,
-#'               trend_model = AR(),
-#'               data = mod_data,
-#'               return_model_data = TRUE,
-#'               burnin = 300,
-#'               samples = 300,
-#'               chains = 2)
+#'              trend_map = trend_map,
+#'              trend_model = AR(),
+#'              data = mod_data,
+#'              return_model_data = TRUE,
+#'              burnin = 300,
+#'              samples = 300,
+#'              chains = 2)
 #'
 #' # The mapping matrix is now supplied as data to the model in the 'Z' element
 #' mod1$model_data$Z
@@ -525,71 +525,6 @@
 #' layout(1)
 #'
 #'
-#' # Example of logistic growth with possible changepoints
-#' # Simple logistic growth model
-#' dNt = function(r, N, k){
-#'    r * N * (k - N)
-#' }
-#'
-#' # Iterate growth through time
-#' Nt = function(r, N, t, k) {
-#' for (i in 1:(t - 1)) {
-#'
-#'  # population at next time step is current population + growth,
-#'  # but we introduce several 'shocks' as changepoints
-#'  if(i %in% c(5, 15, 25, 41, 45, 60, 80)){
-#'    N[i + 1] <- max(1, N[i] + dNt(r + runif(1, -0.1, 0.1),
-#'                                  N[i], k))
-#'    } else {
-#'    N[i + 1] <- max(1, N[i] + dNt(r, N[i], k))
-#'    }
-#'   }
-#'  N
-#' }
-#'
-#' # Simulate expected values
-#' set.seed(11)
-#' expected <- Nt(0.004, 2, 100, 30)
-#' plot(expected, xlab = 'Time')
-#'
-#' # Take Poisson draws
-#' y <- rpois(100, expected)
-#' plot(y, xlab = 'Time')
-#'
-#' # Assemble data into dataframe and model. We set a
-#' # fixed carrying capacity of 35 for this example, but note that
-#' # this value is not required to be fixed at each timepoint
-#' mod_data <- data.frame(y = y,
-#'                        time = 1:100,
-#'                        cap = 35,
-#'                        series = as.factor('series_1'))
-#' plot_mvgam_series(data = mod_data)
-#'
-#' # The intercept is nonidentifiable when using piecewise
-#' # trends because the trend functions have their own offset
-#' # parameters 'm'; it is recommended to always drop intercepts
-#' # when using these trend models
-#' mod <- mvgam(y ~ 0,
-#'              trend_model = PW(growth = 'logistic'),
-#'              family = poisson(),
-#'              data = mod_data,
-#'              burnin = 300,
-#'              samples = 300,
-#'              chains = 2)
-#' summary(mod)
-#'
-#' # Plot the posterior hindcast
-#' plot(mod, type = 'forecast')
-#'
-#' # View the changepoints with ggplot2 utilities
-#' library(ggplot2)
-#' mcmc_plot(mod, variable = 'delta_trend',
-#'           regex = TRUE) +
-#' scale_y_discrete(labels = mod$trend_model$changepoints) +
-#' labs(y = 'Potential changepoint',
-#'      x = 'Rate change')
-#'
-#'
 #' # Example showcasing how cbind() is needed for Binomial observations
 #' # Simulate two time series of Binomial trials
 #' trials <- sample(c(20:25), 50, replace = TRUE)
diff --git a/R/piecewise_trends.R b/R/piecewise_trends.R
index f5997fad..08ffc6d8 100644
--- a/R/piecewise_trends.R
+++ b/R/piecewise_trends.R
@@ -47,7 +47,70 @@
 #' no missing values are allowed in `cap`.
 #'
 #' @rdname piecewise_trends
+#' @examples
+#' \dontrun{
+#' # Example of logistic growth with possible changepoints
+#' # Simple logistic growth model
+#' dNt = function(r, N, k){
+#'    r * N * (k - N)
+#' }
 #'
+#' # Iterate growth through time
+#' Nt = function(r, N, t, k) {
+#' for (i in 1:(t - 1)) {
+#'
+#'  # population at next time step is current population + growth,
+#'  # but we introduce several 'shocks' as changepoints
+#'  if(i %in% c(5, 15, 25, 41, 45, 60, 80)){
+#'    N[i + 1] <- max(1, N[i] + dNt(r + runif(1, -0.1, 0.1),
+#'                                  N[i], k))
+#'    } else {
+#'    N[i + 1] <- max(1, N[i] + dNt(r, N[i], k))
+#'    }
+#'   }
+#'  N
+#' }
+#'
+#' # Simulate expected values
+#' set.seed(11)
+#' expected <- Nt(0.004, 2, 100, 30)
+#' plot(expected, xlab = 'Time')
+#'
+#' # Take Poisson draws
+#' y <- rpois(100, expected)
+#' plot(y, xlab = 'Time')
+#'
+#' # Assemble data into dataframe and model. We set a
+#' # fixed carrying capacity of 35 for this example, but note that
+#' # this value is not required to be fixed at each timepoint
+#' mod_data <- data.frame(y = y,
+#'                        time = 1:100,
+#'                        cap = 35,
+#'                        series = as.factor('series_1'))
+#' plot_mvgam_series(data = mod_data)
+#'
+#' # The intercept is nonidentifiable when using piecewise
+#' # trends because the trend functions have their own offset
+#' # parameters 'm'; it is recommended to always drop intercepts
+#' # when using these trend models
+#' mod <- mvgam(y ~ 0,
+#'              trend_model = PW(growth = 'logistic'),
+#'              family = poisson(),
+#'              data = mod_data,
+#'              chains = 2)
+#' summary(mod)
+#'
+#' # Plot the posterior hindcast
+#' plot(mod, type = 'forecast')
+#'
+#' # View the changepoints with ggplot2 utilities
+#' library(ggplot2)
+#' mcmc_plot(mod, variable = 'delta_trend',
+#'           regex = TRUE) +
+#' scale_y_discrete(labels = mod$trend_model$changepoints) +
+#' labs(y = 'Potential changepoint',
+#'      x = 'Rate change')
+#' }
 #' @export
 PW = function(n_changepoints = 10,
               changepoint_range = 0.8,
diff --git a/man/mvgam.Rd b/man/mvgam.Rd
index 99dfadf2..8f7aafd6 100644
--- a/man/mvgam.Rd
+++ b/man/mvgam.Rd
@@ -474,13 +474,13 @@ trend_map <- data.frame(series = unique(mod_data$series),
 
 # Fit the model using AR1 trends
 mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
-              trend_map = trend_map,
-              trend_model = AR(),
-              data = mod_data,
-              return_model_data = TRUE,
-              burnin = 300,
-              samples = 300,
-              chains = 2)
+             trend_map = trend_map,
+             trend_model = AR(),
+             data = mod_data,
+             return_model_data = TRUE,
+             burnin = 300,
+             samples = 300,
+             chains = 2)
 
 # The mapping matrix is now supplied as data to the model in the 'Z' element
 mod1$model_data$Z
@@ -603,71 +603,6 @@ for(i in 2:50){
 layout(1)
 
 
-# Example of logistic growth with possible changepoints
-# Simple logistic growth model
-dNt = function(r, N, k){
-   r * N * (k - N)
-}
-
-# Iterate growth through time
-Nt = function(r, N, t, k) {
-for (i in 1:(t - 1)) {
-
- # population at next time step is current population + growth,
- # but we introduce several 'shocks' as changepoints
- if(i \%in\% c(5, 15, 25, 41, 45, 60, 80)){
-   N[i + 1] <- max(1, N[i] + dNt(r + runif(1, -0.1, 0.1),
-                                 N[i], k))
-   } else {
-   N[i + 1] <- max(1, N[i] + dNt(r, N[i], k))
-   }
-  }
- N
-}
-
-# Simulate expected values
-set.seed(11)
-expected <- Nt(0.004, 2, 100, 30)
-plot(expected, xlab = 'Time')
-
-# Take Poisson draws
-y <- rpois(100, expected)
-plot(y, xlab = 'Time')
-
-# Assemble data into dataframe and model. We set a
-# fixed carrying capacity of 35 for this example, but note that
-# this value is not required to be fixed at each timepoint
-mod_data <- data.frame(y = y,
-                       time = 1:100,
-                       cap = 35,
-                       series = as.factor('series_1'))
-plot_mvgam_series(data = mod_data)
-
-# The intercept is nonidentifiable when using piecewise
-# trends because the trend functions have their own offset
-# parameters 'm'; it is recommended to always drop intercepts
-# when using these trend models
-mod <- mvgam(y ~ 0,
-             trend_model = PW(growth = 'logistic'),
-             family = poisson(),
-             data = mod_data,
-             burnin = 300,
-             samples = 300,
-             chains = 2)
-summary(mod)
-
-# Plot the posterior hindcast
-plot(mod, type = 'forecast')
-
-# View the changepoints with ggplot2 utilities
-library(ggplot2)
-mcmc_plot(mod, variable = 'delta_trend',
-          regex = TRUE) +
-scale_y_discrete(labels = mod$trend_model$changepoints) +
-labs(y = 'Potential changepoint',
-     x = 'Rate change')
-
-
 # Example showcasing how cbind() is needed for Binomial observations
 # Simulate two time series of Binomial trials
 trials <- sample(c(20:25), 50, replace = TRUE)
diff --git a/man/piecewise_trends.Rd b/man/piecewise_trends.Rd
index aba9914c..bb3bbfd0 100644
--- a/man/piecewise_trends.Rd
+++ b/man/piecewise_trends.Rd
@@ -65,6 +65,71 @@ transformed if you are using a \code{poisson()} or \code{nb()} family). It is th
 that you specify the \code{cap} values on the scale of your outcome. Note also that
 no missing values are allowed in \code{cap}.
 }
+\examples{
+\dontrun{
+# Example of logistic growth with possible changepoints
+# Simple logistic growth model
+dNt = function(r, N, k){
+   r * N * (k - N)
+}
+
+# Iterate growth through time
+Nt = function(r, N, t, k) {
+for (i in 1:(t - 1)) {
+
+ # population at next time step is current population + growth,
+ # but we introduce several 'shocks' as changepoints
+ if(i \%in\% c(5, 15, 25, 41, 45, 60, 80)){
+   N[i + 1] <- max(1, N[i] + dNt(r + runif(1, -0.1, 0.1),
+                                 N[i], k))
+   } else {
+   N[i + 1] <- max(1, N[i] + dNt(r, N[i], k))
+   }
+  }
+ N
+}
+
+# Simulate expected values
+set.seed(11)
+expected <- Nt(0.004, 2, 100, 30)
+plot(expected, xlab = 'Time')
+
+# Take Poisson draws
+y <- rpois(100, expected)
+plot(y, xlab = 'Time')
+
+# Assemble data into dataframe and model. We set a
+# fixed carrying capacity of 35 for this example, but note that
+# this value is not required to be fixed at each timepoint
+mod_data <- data.frame(y = y,
+                       time = 1:100,
+                       cap = 35,
+                       series = as.factor('series_1'))
+plot_mvgam_series(data = mod_data)
+
+# The intercept is nonidentifiable when using piecewise
+# trends because the trend functions have their own offset
+# parameters 'm'; it is recommended to always drop intercepts
+# when using these trend models
+mod <- mvgam(y ~ 0,
+             trend_model = PW(growth = 'logistic'),
+             family = poisson(),
+             data = mod_data,
+             chains = 2)
+summary(mod)
+
+# Plot the posterior hindcast
+plot(mod, type = 'forecast')
+
+# View the changepoints with ggplot2 utilities
+library(ggplot2)
+mcmc_plot(mod, variable = 'delta_trend',
+          regex = TRUE) +
+scale_y_discrete(labels = mod$trend_model$changepoints) +
+labs(y = 'Potential changepoint',
+     x = 'Rate change')
+}
+}
 \references{
 Taylor, Sean J., and Benjamin Letham. "Forecasting at scale." The American Statistician 72.1 (2018): 37-45.
 }
diff --git a/src/mvgam.dll b/src/mvgam.dll
index c1becdd1..b908ba0c 100644
Binary files a/src/mvgam.dll and b/src/mvgam.dll differ