diff --git a/R/detection_calculate.R b/R/detection_calculate.R
index f4d9a09..d073388 100644
--- a/R/detection_calculate.R
+++ b/R/detection_calculate.R
@@ -62,7 +62,7 @@
 #'                     cov_val = c(0,0), pcr_n = 3)
 #'
 #' # Calculate mu_critical at salinity 0.5 z-scores greater than the mean
-#' detection_calculate(fit.cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
+#' detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
 #'                     cov_val = c(0,0.5), pcr_n = 3)
 #'
 #' # Ex. 2: Calculating necessary effort for detection with multiple traditional
diff --git a/R/mu_critical.R b/R/mu_critical.R
index 5630f77..65c3fbd 100644
--- a/R/mu_critical.R
+++ b/R/mu_critical.R
@@ -48,8 +48,8 @@
 #'
 #' # Fit a model including 'Filter_time' and 'Salinity' site-level covariates
 #' fit_cov <- joint_model(data = goby_data, cov = c('Filter_time','Salinity'),
-#'                       family = "poisson", p10_priors = c(1,20), q = FALSE,
-#'                       multicore = FALSE)
+#'                        family = "poisson", p10_priors = c(1,20), q = FALSE,
+#'                        multicore = FALSE)
 #'
 #' # Calculate mu_critical at the mean covariate values (covariates are
 #' # standardized, so mean = 0)
diff --git a/R/traditional_model.R b/R/traditional_model.R
index 27be5f0..519d549 100644
--- a/R/traditional_model.R
+++ b/R/traditional_model.R
@@ -92,23 +92,23 @@
 #' @examples
 #' \donttest{
 #' # Load data
-#' data(greencrabData)
+#' data(green_crab_data)
 #'
 #' # Examine data in list
 #' # This function uses only traditional survey count data and optionally
 #' # the count type data
-#' names(greencrabData)
+#' names(green_crab_data)
 #'
 #' # Note that the surveyed sites (rows) should match in the data
-#' dim(greencrabData$count)[1]
-#' dim(greencrabData$count_type)[1]
+#' dim(green_crab_data$count)[1]
+#' dim(green_crab_data$count_type)[1]
 #'
 #' # Fit a model without estimating a gear scaling coefficient for traditional
 #' # survey gear types.
 #' # This model assumes all traditional survey methods have the same
 #' # catchability.
 #' # Count data is modeled using a poisson distribution.
-#' fit_no_q <- traditional_model(data = greencrabData, family = "poisson",
+#' fit_no_q <- traditional_model(data = green_crab_data, family = "poisson",
 #'                               q = FALSE, phi_priors = NULL,
 #'                               multicore = FALSE, verbose = TRUE)
 #'
@@ -118,11 +118,12 @@
 #' # This model does not assume all traditional survey methods have the same
 #' # catchability.
 #' # Count data is modeled using a negative binomial distribution.
-#' fit_q <- traditional_model(data = greencrabData, family = "negbin", q = TRUE,
-#'                            phi_priors = c(0.25,0.25), multicore = FALSE,
-#'                            initial_values = NULL, n_chain = 4,
-#'                            n_warmup = 500, n_iter = 3000, thin = 1,
-#'                            adapt_delta = 0.9, verbose = TRUE, seed = 123)
+#' fit_q <- traditional_model(data = green_crab_data, family = "negbin",
+#'                            q = TRUE, phi_priors = c(0.25,0.25),
+#'                            multicore = FALSE, initial_values = NULL,
+#'                            n_chain = 4, n_warmup = 500, n_iter = 3000,
+#'                            thin = 1, adapt_delta = 0.9, verbose = TRUE,
+#'                            seed = 123)
 #' }
 #'
 
diff --git a/man/detection_calculate.Rd b/man/detection_calculate.Rd
index 629728e..f4e98a8 100644
--- a/man/detection_calculate.Rd
+++ b/man/detection_calculate.Rd
@@ -78,7 +78,7 @@ detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                     cov_val = c(0,0), pcr_n = 3)
 
 # Calculate mu_critical at salinity 0.5 z-scores greater than the mean
-detection_calculate(fit.cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
+detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
                     cov_val = c(0,0.5), pcr_n = 3)
 
 # Ex. 2: Calculating necessary effort for detection with multiple traditional
diff --git a/man/mu_critical.Rd b/man/mu_critical.Rd
index 1123a1e..84fa81c 100644
--- a/man/mu_critical.Rd
+++ b/man/mu_critical.Rd
@@ -56,8 +56,8 @@ data(goby_data)
 
 # Fit a model including 'Filter_time' and 'Salinity' site-level covariates
 fit_cov <- joint_model(data = goby_data, cov = c('Filter_time','Salinity'),
-                      family = "poisson", p10_priors = c(1,20), q = FALSE,
-                      multicore = FALSE)
+                       family = "poisson", p10_priors = c(1,20), q = FALSE,
+                       multicore = FALSE)
 
 # Calculate mu_critical at the mean covariate values (covariates are
 # standardized, so mean = 0)
diff --git a/man/traditional_model.Rd b/man/traditional_model.Rd
index a7d36e2..fa645c2 100644
--- a/man/traditional_model.Rd
+++ b/man/traditional_model.Rd
@@ -114,23 +114,23 @@ If any of these checks fail, the function returns an error message.
 \examples{
 \donttest{
 # Load data
-data(greencrabData)
+data(green_crab_data)
 
 # Examine data in list
 # This function uses only traditional survey count data and optionally
 # the count type data
-names(greencrabData)
+names(green_crab_data)
 
 # Note that the surveyed sites (rows) should match in the data
-dim(greencrabData$count)[1]
-dim(greencrabData$count_type)[1]
+dim(green_crab_data$count)[1]
+dim(green_crab_data$count_type)[1]
 
 # Fit a model without estimating a gear scaling coefficient for traditional
 # survey gear types.
 # This model assumes all traditional survey methods have the same
 # catchability.
 # Count data is modeled using a poisson distribution.
-fit_no_q <- traditional_model(data = greencrabData, family = "poisson",
+fit_no_q <- traditional_model(data = green_crab_data, family = "poisson",
                               q = FALSE, phi_priors = NULL,
                               multicore = FALSE, verbose = TRUE)
 
@@ -140,11 +140,12 @@ fit_no_q <- traditional_model(data = greencrabData, family = "poisson",
 # This model does not assume all traditional survey methods have the same
 # catchability.
 # Count data is modeled using a negative binomial distribution.
-fit_q <- traditional_model(data = greencrabData, family = "negbin", q = TRUE,
-                           phi_priors = c(0.25,0.25), multicore = FALSE,
-                           initial_values = NULL, n_chain = 4,
-                           n_warmup = 500, n_iter = 3000, thin = 1,
-                           adapt_delta = 0.9, verbose = TRUE, seed = 123)
+fit_q <- traditional_model(data = green_crab_data, family = "negbin",
+                           q = TRUE, phi_priors = c(0.25,0.25),
+                           multicore = FALSE, initial_values = NULL,
+                           n_chain = 4, n_warmup = 500, n_iter = 3000,
+                           thin = 1, adapt_delta = 0.9, verbose = TRUE,
+                           seed = 123)
 }
 
 }