diff --git a/R/rbin.R b/R/rbin.R
index 166c093..eec8108 100644
--- a/R/rbin.R
+++ b/R/rbin.R
@@ -102,40 +102,28 @@
 #' beta_coefficients <- 0.2
 #' latent_correlation_matrix <- toeplitz(c(1, 0.9, 0.9, 0.9))
 #' x <- rep(rnorm(sample_size), each = cluster_size)
-#' simulated_binary_dataset <- rbin(
-#'   clsize = cluster_size,
+#' simulated_binary_dataset <- rbin(clsize = cluster_size,
 #'   intercepts = beta_intercepts, betas = beta_coefficients,
-#'   xformula = ~x, cor.matrix = latent_correlation_matrix, link = "probit"
-#' )
+#'   xformula = ~x, cor.matrix = latent_correlation_matrix, link = "probit")
 #' library(gee)
-#' binary_gee_model <- gee(y ~ x,
-#'   family = binomial("probit"), id = id,
-#'   data = simulated_binary_dataset$simdata
-#' )
+#' binary_gee_model <- gee(y ~ x, family = binomial("probit"), id = id,
+#'   data = simulated_binary_dataset$simdata)
 #' summary(binary_gee_model)$coefficients
 #'
 #' ## See Example 3.6 in the Vignette.
 #' set.seed(8)
 #' library(evd)
-#' simulated_latent_variables1 <- rmvevd(sample_size,
-#'   dep = sqrt(1 - 0.9),
-#'   model = "log", d = cluster_size
-#' )
-#' simulated_latent_variables2 <- rmvevd(sample_size,
-#'   dep = sqrt(1 - 0.9),
-#'   model = "log", d = cluster_size
-#' )
+#' simulated_latent_variables1 <- rmvevd(sample_size, dep = sqrt(1 - 0.9),
+#'   model = "log", d = cluster_size)
+#'   simulated_latent_variables2 <- rmvevd(sample_size, dep = sqrt(1 - 0.9),
+#'   model = "log", d = cluster_size)
 #' simulated_latent_variables <- simulated_latent_variables1 -
 #'   simulated_latent_variables2
-#' simulated_binary_dataset <- rbin(
-#'   clsize = cluster_size,
+#' simulated_binary_dataset <- rbin(clsize = cluster_size,
 #'   intercepts = beta_intercepts, betas = beta_coefficients,
-#'   xformula = ~x, rlatent = simulated_latent_variables
-#' )
-#' binary_gee_model <- gee(y ~ x,
-#'   family = binomial("logit"), id = id,
-#'   data = simulated_binary_dataset$simdata
-#' )
+#'   xformula = ~x, rlatent = simulated_latent_variables)
+#' binary_gee_model <- gee(y ~ x, family = binomial("logit"), id = id,
+#'   data = simulated_binary_dataset$simdata)
 #' summary(binary_gee_model)$coefficients
 #' @export
 rbin <- function(clsize = clsize, intercepts = intercepts, betas = betas,
diff --git a/man/rbin.Rd b/man/rbin.Rd
index fbdb20d..8fb2990 100644
--- a/man/rbin.Rd
+++ b/man/rbin.Rd
@@ -99,40 +99,28 @@ beta_intercepts <- 0
 beta_coefficients <- 0.2
 latent_correlation_matrix <- toeplitz(c(1, 0.9, 0.9, 0.9))
 x <- rep(rnorm(sample_size), each = cluster_size)
-simulated_binary_dataset <- rbin(
-  clsize = cluster_size,
+simulated_binary_dataset <- rbin(clsize = cluster_size,
   intercepts = beta_intercepts, betas = beta_coefficients,
-  xformula = ~x, cor.matrix = latent_correlation_matrix, link = "probit"
-)
+  xformula = ~x, cor.matrix = latent_correlation_matrix, link = "probit")
 library(gee)
-binary_gee_model <- gee(y ~ x,
-  family = binomial("probit"), id = id,
-  data = simulated_binary_dataset$simdata
-)
+binary_gee_model <- gee(y ~ x, family = binomial("probit"), id = id,
+  data = simulated_binary_dataset$simdata)
 summary(binary_gee_model)$coefficients
 
 ## See Example 3.6 in the Vignette.
 set.seed(8)
 library(evd)
-simulated_latent_variables1 <- rmvevd(sample_size,
-  dep = sqrt(1 - 0.9),
-  model = "log", d = cluster_size
-)
-simulated_latent_variables2 <- rmvevd(sample_size,
-  dep = sqrt(1 - 0.9),
-  model = "log", d = cluster_size
-)
+simulated_latent_variables1 <- rmvevd(sample_size, dep = sqrt(1 - 0.9),
+  model = "log", d = cluster_size)
+  simulated_latent_variables2 <- rmvevd(sample_size, dep = sqrt(1 - 0.9),
+  model = "log", d = cluster_size)
 simulated_latent_variables <- simulated_latent_variables1 -
   simulated_latent_variables2
-simulated_binary_dataset <- rbin(
-  clsize = cluster_size,
+simulated_binary_dataset <- rbin(clsize = cluster_size,
   intercepts = beta_intercepts, betas = beta_coefficients,
-  xformula = ~x, rlatent = simulated_latent_variables
-)
-binary_gee_model <- gee(y ~ x,
-  family = binomial("logit"), id = id,
-  data = simulated_binary_dataset$simdata
-)
+  xformula = ~x, rlatent = simulated_latent_variables)
+binary_gee_model <- gee(y ~ x, family = binomial("logit"), id = id,
+  data = simulated_binary_dataset$simdata)
 summary(binary_gee_model)$coefficients
 }
 \references{