diff --git a/.Rbuildignore b/.Rbuildignore
index 8e7ee7ff..c6b10c45 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -9,3 +9,4 @@
 ^README\.Rmd$
 ^README-.*\.png$
 ^\.github$
+^R/util-intern_pkg_man\.r$
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 8d60ca8f..96316318 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -47,3 +47,4 @@ jobs:
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true
+          error-on: '"error"'
diff --git a/DESCRIPTION b/DESCRIPTION
index 8e73381f..a4faecc9 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -10,31 +10,32 @@ Imports:
 		data.table,
 		dplyr,
 		fasterize,
-		fasttime,
 		gdalUtilities,
 		geodist,
 		gdistance,
 		geosphere,
 		jsonlite,
-		knitr,
 		lubridate,
 		magrittr,
-		methods,
 		plotrix,
 		purrr,
 		raster,
 		readxl,
-		rmarkdown,
 		sf,
 		sp,
 		tibble,
 		tidyr,
+		units,
 		zip
 Suggests: 
 		gganimate,
 		gifski,
+		knitr,
+		methods,
 		png,
-    testthat (>= 3.0.0),
+		rmarkdown,
+		terra,
+		testthat (>= 3.0.0),
 		tint
 URL: https://github.com/ocean-tracking-network/glatos
 BugReports: https://github.com/ocean-tracking-network/glatos/issues
diff --git a/R/class-glatos_animals.r b/R/class-glatos_animals.r
index 998b0b5d..a2de0ad5 100644
--- a/R/class-glatos_animals.r
+++ b/R/class-glatos_animals.r
@@ -71,6 +71,8 @@
 #' ga_tbl <- as_glatos_animals(x_tbl)
 #'
 #'
+#' # All below will error as invalid
+#'
 #' # data.frame input; missing column name
 #' library(dplyr) # for rename
 #' x2 <- rename(x,
@@ -78,17 +80,19 @@
 #'   release_timestamp = utc_release_date_time
 #' )
 #'
-#' ga2 <- as_glatos_animals(x2)
-#'
+#' try(
+#'   ga2 <- as_glatos_animals(x2)
+#' )
 #'
-#' # data.grame input; wrong column class
+#' # data.frame input; wrong column class
 #' x3 <- mutate(x,
 #'   animal_id = as.integer(animal_id),
 #'   utc_release_date_time = as.character(utc_release_date_time)
 #' )
 #'
-#' ga3 <- as_glatos_animals(x3)
-#'
+#' try(
+#'   ga3 <- as_glatos_animals(x3)
+#' )
 #'
 #' # Validation and checking
 #'
diff --git a/R/class-glatos_detections.r b/R/class-glatos_detections.r
index 5905bcb5..3856b770 100644
--- a/R/class-glatos_detections.r
+++ b/R/class-glatos_detections.r
@@ -73,6 +73,8 @@
 #' gd_tbl <- as_glatos_detections(x_tbl)
 #'
 #'
+#' # All below will error as invalid
+#'
 #' # data.frame input; missing column name
 #' library(dplyr) # for rename
 #' x2 <- rename(x,
@@ -80,8 +82,9 @@
 #'   det_date_time = detection_timestamp_utc
 #' )
 #'
-#' gd2 <- as_glatos_detections(x2)
-#'
+#' try(
+#'   gd2 <- as_glatos_detections(x2)
+#' )
 #'
 #' # data.frame input; wrong column class
 #' x3 <- mutate(x,
@@ -89,8 +92,9 @@
 #'   detection_timestamp_utc = as.character(detection_timestamp_utc)
 #' )
 #'
-#' gr3 <- as_glatos_detections(x3)
-#'
+#' try(
+#'   gr3 <- as_glatos_detections(x3)
+#' )
 #'
 #' # Validation and checking
 #'
diff --git a/R/class-glatos_receivers.r b/R/class-glatos_receivers.r
index 3d0b3786..d8f6c39a 100644
--- a/R/class-glatos_receivers.r
+++ b/R/class-glatos_receivers.r
@@ -86,6 +86,7 @@
 #'
 #' gr_tbl <- as_glatos_receivers(x_tbl)
 #'
+#' # All below will error as invalid
 #'
 #' # data.frame input; missing column name
 #' library(dplyr) # for rename
@@ -94,8 +95,9 @@
 #'   deploy_timestamp = deploy_date_time
 #' )
 #'
-#' gr2 <- as_glatos_receivers(x2)
-#'
+#' try(
+#'   gr2 <- as_glatos_receivers(x2)
+#' )
 #'
 #' # data.frame input; wrong column class
 #' x3 <- mutate(x,
@@ -103,8 +105,9 @@
 #'   deploy_date_time = as.character(deploy_date_time)
 #' )
 #'
-#' gr3 <- as_glatos_receivers(x3)
-#'
+#' try(
+#'   gr3 <- as_glatos_receivers(x3)
+#' )
 #'
 #' # Validation and checking
 #'
diff --git a/R/load-read_glatos_detections.r b/R/load-read_glatos_detections.r
index d53e77c9..a4b3d9f1 100644
--- a/R/load-read_glatos_detections.r
+++ b/R/load-read_glatos_detections.r
@@ -53,9 +53,9 @@ read_glatos_detections <- function(det_file, version = NULL) {
   # Identify detection file version
   id_det_version <- function(det_file) {
     det_col_names <- names(data.table::fread(det_file, nrows = 0))
-    if (all(glatos:::glatos_detection_schema$v1.4$name %in% det_col_names)) {
+    if (all(glatos_detection_schema$v1.4$name %in% det_col_names)) {
       return("1.4")
-    } else if (all(glatos:::glatos_detection_schema$v1.3$name %in% det_col_names)) {
+    } else if (all(glatos_detection_schema$v1.3$name %in% det_col_names)) {
       return("1.3")
     } else {
       stop("Detection file version could not be identified.")
@@ -65,7 +65,7 @@ read_glatos_detections <- function(det_file, version = NULL) {
   if (is.null(version)) {
     version <- id_det_version(det_file)
   } else if (!(paste0("v", version) %in%
-    names(glatos:::glatos_detection_schema))) {
+    names(glatos_detection_schema))) {
     stop(paste0("Detection file version ", version, " is not supported."))
   }
 
@@ -73,7 +73,7 @@ read_glatos_detections <- function(det_file, version = NULL) {
   if (version %in% c("1.3", "1.4")) {
     vversion <- paste0("v", version)
 
-    col_classes <- glatos:::glatos_detection_schema[[vversion]]$type
+    col_classes <- glatos_detection_schema[[vversion]]$type
     timestamp_cols <- which(col_classes == "POSIXct")
     date_cols <- which(col_classes == "Date")
     col_classes[c(timestamp_cols, date_cols)] <- "character"
@@ -89,9 +89,9 @@ read_glatos_detections <- function(det_file, version = NULL) {
     options(lubridate.fasttime = TRUE)
     for (j in timestamp_cols) {
       data.table::set(dtc,
-        j = glatos:::glatos_detection_schema[[vversion]]$name[j],
+        j = glatos_detection_schema[[vversion]]$name[j],
         value = lubridate::parse_date_time(
-          dtc[[glatos:::glatos_detection_schema[[vversion]]$name[j]]],
+          dtc[[glatos_detection_schema[[vversion]]$name[j]]],
           orders = "ymd HMS",
           tz = "UTC"
         )
@@ -100,15 +100,15 @@ read_glatos_detections <- function(det_file, version = NULL) {
     # coerce dates to date
     for (j in date_cols) {
       data.table::set(dtc,
-        j = glatos:::glatos_detection_schema[[vversion]]$name[j],
-        value = ifelse(dtc[[glatos:::glatos_detection_schema[[vversion]]$name[j]]] == "",
+        j = glatos_detection_schema[[vversion]]$name[j],
+        value = ifelse(dtc[[glatos_detection_schema[[vversion]]$name[j]]] == "",
           NA,
-          dtc[[glatos:::glatos_detection_schema[[vversion]]$name[j]]]
+          dtc[[glatos_detection_schema[[vversion]]$name[j]]]
         )
       )
       data.table::set(dtc,
-        j = glatos:::glatos_detection_schema[[vversion]]$name[j],
-        value = as.Date(dtc[[glatos:::glatos_detection_schema[[vversion]]$name[j]]])
+        j = glatos_detection_schema[[vversion]]$name[j],
+        value = as.Date(dtc[[glatos_detection_schema[[vversion]]$name[j]]])
       )
     }
   }
diff --git a/R/load-read_glatos_receivers.r b/R/load-read_glatos_receivers.r
index a5084d5a..5b45e328 100644
--- a/R/load-read_glatos_receivers.r
+++ b/R/load-read_glatos_receivers.r
@@ -49,9 +49,9 @@ read_glatos_receivers <- function(rec_file, version = NULL) {
   # Identify file version
   id_file_version <- function(rec_file) {
     col_names <- names(data.table::fread(rec_file, nrows = 0))
-    if (all(glatos:::glatos_receivers_schema$v1.1$name %in% col_names)) {
+    if (all(glatos_receivers_schema$v1.1$name %in% col_names)) {
       return("1.1")
-    } else if (all(glatos:::glatos_receivers_schema$v1.0$name %in% col_names)) {
+    } else if (all(glatos_receivers_schema$v1.0$name %in% col_names)) {
       return("1.0")
     } else {
       stop("Receiver location file version could not be identified.")
@@ -61,7 +61,7 @@ read_glatos_receivers <- function(rec_file, version = NULL) {
   if (is.null(version)) {
     version <- id_file_version(rec_file)
   } else if (!(paste0("v", version) %in%
-    names(glatos:::glatos_receivers_schema))) {
+    names(glatos_receivers_schema))) {
     stop(paste0(
       "Receiver locations file version ", version,
       " is not supported."
@@ -72,7 +72,7 @@ read_glatos_receivers <- function(rec_file, version = NULL) {
   if (version %in% c("1.0", "1.1")) {
     ver_txt <- paste0("v", version)
 
-    col_classes <- glatos:::glatos_receivers_schema[[ver_txt]]$type
+    col_classes <- glatos_receivers_schema[[ver_txt]]$type
     timestamp_cols <- which(col_classes == "POSIXct")
     date_cols <- which(col_classes == "Date")
     col_classes[c(timestamp_cols, date_cols)] <- "character"
diff --git a/R/load-read_glatos_workbook.r b/R/load-read_glatos_workbook.r
index 6ba58b8b..b8ea3866 100644
--- a/R/load-read_glatos_workbook.r
+++ b/R/load-read_glatos_workbook.r
@@ -109,7 +109,7 @@ read_glatos_workbook <- function(
 
   # Identify workbook version (based on sheet names)
   id_workbook_version <- function(wb_file, sheets) {
-    if (all(names(glatos:::glatos_workbook_schema$v1.3) %in% sheets)) {
+    if (all(names(glatos_workbook_schema$v1.3) %in% sheets)) {
       return("1.3")
     } else {
       stop(paste0(
@@ -124,7 +124,7 @@ read_glatos_workbook <- function(
   if (is.null(wb_version)) {
     wb_version <- id_workbook_version(wb_file, sheets)
   } else if (!(paste0("v", wb_version) %in%
-    names(glatos:::glatos_workbook_schema))) {
+    names(glatos_workbook_schema))) {
     stop(paste0("Workbook version ", wb_version, " is not supported."))
   }
 
@@ -134,7 +134,7 @@ read_glatos_workbook <- function(
 
   #-Workbook v1.3--------------------------------------------------------------
   if (wb_version == "1.3") {
-    wb[names(glatos:::glatos_workbook_schema$v1.3)] <- NA
+    wb[names(glatos_workbook_schema$v1.3)] <- NA
 
     # Get project data
     tmp <- tryCatch(
@@ -170,18 +170,18 @@ read_glatos_workbook <- function(
     # Read all sheets except project
     if (read_all) {
       sheets_to_read <- sheets
-      extra_sheets <- setdiff(sheets, names(glatos:::glatos_workbook_schema[[
+      extra_sheets <- setdiff(sheets, names(glatos_workbook_schema[[
         paste0("v", wb_version)
       ]]))
     } else {
-      sheets_to_read <- names(glatos:::glatos_workbook_schema[[
+      sheets_to_read <- names(glatos_workbook_schema[[
         paste0("v", wb_version)
       ]])
     }
     sheets_to_read <- setdiff(sheets_to_read, "project") # exclude project
 
     for (i in 1:length(sheets_to_read)) {
-      schema_i <- glatos:::glatos_workbook_schema[[
+      schema_i <- glatos_workbook_schema[[
         paste0("v", wb_version)
       ]][[sheets_to_read[i]]]
 
@@ -532,7 +532,7 @@ read_glatos_workbook <- function(
   # assign classes
   wb2$animals <- as_glatos_animals(wb2$animals)
   wb2$receivers <- as_glatos_receivers(wb2$receivers)
-  wb2 <- glatos:::glatos_workbook(wb2)
+  wb2 <- glatos_workbook(wb2)
 
   return(wb2)
 }
diff --git a/R/load-read_otn_deployments.R b/R/load-read_otn_deployments.R
index 774d4c0e..5f4aa9d7 100644
--- a/R/load-read_otn_deployments.R
+++ b/R/load-read_otn_deployments.R
@@ -31,11 +31,13 @@
 #' @author A. Nunes, \email{anunes@dal.ca}
 #'
 #' @examples
+#' \dontrun{
 #' # get path to example deployments file
 #' deployment_file <- system.file("extdata", "hfx_deployments.csv",
 #'   package = "glatos"
 #' )
 #' dep <- read_otn_deployments(deployment_file)
+#' }
 #'
 #' @importFrom lubridate parse_date_time
 #' @importFrom tidyr extract
diff --git a/R/load-read_vdat_csv.r b/R/load-read_vdat_csv.r
index d673dc18..ccc3230a 100644
--- a/R/load-read_vdat_csv.r
+++ b/R/load-read_vdat_csv.r
@@ -1,260 +1,289 @@
-#' Read data from an Innovasea Fathom VDAT CSV file
-#'
-#' Read data from an Innovasea Fathom VDAT CSV file
-#'
-#' @param src A character string with path and name of an Innovasea VDAT CSV
-#'   detection file. If only file name is given, then the file must be located
-#'   in the working directory.
-#'
-#' @param record_types An optional vector of character strings with names of
-#'   record types to read from the file. E.g., "DET" for detection records.
-#'   Default (\code{NULL}) will read all record types present in input CSV
-#'   \code{src}.
-#'
-#' @param show_progress Optional argument passed to \code{data.table::fread}'s
-#'   \code{showProgress}.
-#'
-#' @details Reading is done via \code{\link[data.table]{fread}}.
-#'
-#' @details All timestamp columns are assumed to be in UTC and are assigned
-#'   class \code{POSIXct}. The internal value of timestamps will include
-#'   fractional seconds but the printed value (i.e., displayed or written to
-#'   file) will be truncated according to \code{options()$digits.secs}. By
-#'   default (\code{options()$digits.secs = NULL}), values are truncated (i.e.,
-#'   rounded down) to the nearest second. To maintain the full resolution
-#'   present in the input Fathom CSV file, set \code{options(digits.secs = 6)}.
-#'
-#' @return A list of class \code{vdat_list} with one named element for each
-#'   record type and attributes: \code{fathom_csv_format_version} with version
-#'   of the input Fathom CSV format; \code{source} with version of
-#'   VDAT.exe used to create the input file.
-#'
-#'
-#' @author C. Holbrook (cholbrook@@usgs.gov)
-#'
-#' @examples
-#' 
-#' # Example 1. Read a single file
-#' 
-#' vrl_file <- system.file("extdata", "detection_files_raw", 
-#'   "VR2W_109924_20110718_1.vrl", package="glatos")
-#'
-#' temp_dir <- tempdir()
-#'
-#' csv_file <- vdat_convert(vrl_file, out_dir = temp_dir)
-#'
-#' #utils::browseURL(temp_dir)
-#'
-#' #read all record types
-#' vdat <- read_vdat_csv(csv_file)
-#'
-#' #read only one record type
-#' vdat <- read_vdat_csv(csv_file, record_types = c("DET"))
-#'  
-#'
-#' # Example 2. Read and combine detection records from multiple files
-#' 
-#' # get two example files
-#' vrl_files <- system.file("extdata", "detection_files_raw",
-#'   c("VR2W_109924_20110718_1.vrl", 
-#'     "HR2-180 461396 2021-04-20 173145.vdat"), 
-#'   package="glatos")
-#' 
-#' csv_files <- vdat_convert(vrl_files, out_dir = temp_dir)
-#'
-#' 
-#' # using dplyr
-#' 
-#' library(dplyr)
-#' 
-#' # basic steps: import each to list element, subset DET records, 
-#'                add column with source file name, combine.
-#'                
-#' det2_tbl <- csv_files %>% 
-#'              lapply(
-#'                function(x) {
-#'                  read_vdat_csv(x, record_types = "DET")$DET %>% 
-#'                  as_tibble %>%
-#'                  mutate(source_file = basename(x))
-#'                }) %>% 
-#'              bind_rows
-#' 
-#' 
-#' #using data.table
-#' 
-#' library(data.table)
-#' 
-#' det2_dt <- rbindlist(
-#'              lapply(csv_files, 
-#'                function(x) {
-#'                 read_vdat_csv(x, record_types = "DET")$DET[, 
-#'                   source_file := basename(x)]
-#'              }))
-#'              
-#' \dontrun{
-#' 
-#' # get current version of digits.secs
-#' op_digits.secs <- options()$digits.secs
-#'   
-#' # set digits.secs = NULL (default, truncates to nearest second)
-#' options(digits.secs = NULL)
-#' 
-#' # note truncation to nearest second
-#' vdat$DET$Time[2]
-#' 
-#' # set digits.secs to see fractional seconds
-#' options(digits.secs = 6)
-#' 
-#' # note fractional seconds
-#' vdat$DET$Time[2]
-#' 
-#' # return to default values
-#' options(digits.secs = op_digits.secs)
-#' 
-#' # or specify via format %OSn e.g., when writing to disk or external database
-#' # see ?strptime
-#' format(vdat$DET$Time[2], format = "%Y-%m-%d %H:%M:%OS6")
-#' 
-#' }
-#'
-#' @export
-read_vdat_csv <- function(src, 
-                          record_types = NULL,
-                          show_progress = FALSE){
-  
-  #Check if exists
-  if(!file.exists(src)){
-    warning("File not found: ", src)
-    return()
-  }
-  
-  #Identify vdat csv format version and vdat.exe version that created input csv
-  vdat_header <- data.table::fread(file = src, nrows = 1L, header = FALSE)
-  
-  #Check if fathom csv format (error if looks like VUE export format)
-  if(vdat_header$V1[1] == "VEMCO DATA LOG") {
-    # Set column names
-    src_version <- data.table::data.table(fathom_csv = vdat_header$V2[1],
-                                          vdat_exe = vdat_header$V3[1])
-  } else if (all(c("Receiver", 
-                   "Transmitter", 
-                   "Transmitter Name", 
-                   "Transmitter Serial", 
-                   "Sensor Value", 
-                   "Sensor Unit", 
-                   "Station Name", 
-                   "Latitude", 
-                   "Longitude") %in% 
-             as.character(vdat_header))) {
-      stop("Input file appears to be in VUE Export format, which is not ",
-           "supported.\n Only Fathom CSV format is supported. \n",
-           " Perhaps you want read_vue_detection_csv()?")
-    }
-  
-  #Read all data into character vector (like readLines)
-  vdat_txt <- data.table::fread(file = src, skip = 2, header = FALSE, 
-                                sep = NULL, col.names = "txt", 
-                                showProgress = show_progress)
-  
-  #Identify record type of each row
-  vdat_txt[ , record_type := data.table::fread(file = src, 
-                                               skip = 2, 
-                                               header = FALSE, 
-                                               sep = ",", 
-                                               select = 1, 
-                                               fill = TRUE, 
-                                               showProgress = show_progress)]
-
-  #Drop _DESC from headers
-  vdat_txt[ , record_type := gsub("_DESC$", "", record_type)]  
-  
-  
-  #Get record identifiers from csv file
-  csv_record_types <- unique(vdat_txt$record_type)
-
-
-  if(is.null(record_types)) {
-    
-    record_types <- csv_record_types 
-
-  } else {
-    
-    #Check if any record_types are not in csv
-    unknown_record_types <- setdiff(record_types, csv_record_types)
-    
-    if(length(unknown_record_types) > 0) stop("The following input ",
-                                       "'record_types' ",
-                                       "were not found in CSV file: \n\t",
-                                       paste(unknown_record_types, 
-                                             collapse = ", "))
-  }  
-    
-  #Drop data types not requested by user
-  vdat_txt <- vdat_txt[record_type %in% record_types]
-  
-  #Split into list elements by record type
-  vdat_list <- split(vdat_txt, 
-                    by = "record_type",
-                    keep.by = FALSE)
-  
-  data(vdat_csv_schema)
-  
-  vdat_csv_schema <- vdat_csv_schema[[paste0("v", src_version$fathom_csv)]]
-  
- 
-  # Preallocate list; element = record type
-  vdat <- setNames(object = vector("list", length(vdat_list)),
-                   nm = names(vdat_list))
-  
-  for(i in 1:length(vdat)){ 
-    
-    # fread has issues with numerical precision (e.g., 'Time Correction (s)')
-    #  so read all columns as character then coerce 
-    vdat[[i]] <- data.table::fread(
-                   text = paste0(c(vdat_list[[i]]$txt,""), 
-                                 collapse = "\n"), 
-                   sep = ",", na.strings = "",
-                   colClasses = "character",
-                   header = TRUE,
-                   drop = 1, 
-                   showProgress = show_progress)
-    
-    # Coerce to class
-    schema_i <- vdat_csv_schema[[names(vdat[i])]]
-    
-    # numeric
-    numeric_cols <- schema_i$name[schema_i$type == "numeric"]
-    
-    if(length(numeric_cols) > 0) {
-      vdat[[i]][, (numeric_cols) := lapply(.SD, as.numeric), 
-                .SDcols = numeric_cols]
-    }
-    
-    # POSIXct
-    timestamp_cols <- schema_i$name[schema_i$type == "POSIXct"]
-    
-    if(length(timestamp_cols) > 0){
-      vdat[[i]][, (timestamp_cols) := lapply(.SD, 
-                                             function(x) 
-                                               lubridate::fast_strptime(
-                                                 x, 
-                                                 format = "%Y-%m-%d %H:%M:%OS",                     
-                                                 lt = FALSE)), 
-                .SDcols = timestamp_cols] 
-    }
-    
-    # Assign class
-    new_class <- c(paste0("vdat_", names(vdat[i])), class(vdat[[i]]))
-    data.table::setattr(vdat[[i]], "class", new_class)
-    
-  } # end i
-  
-  #Assign class and other attributes
-  vdat_list <- structure(vdat, 
-                         class = c("vdat_list", class(vdat)),
-                         fathom_csv_version = src_version$fathom_csv,
-                         source = src_version$vdat_exe)
-  
-  return(vdat_list)
-}  
-  
\ No newline at end of file
+#' Read data from an Innovasea Fathom VDAT CSV file
+#'
+#' Read data from an Innovasea Fathom VDAT CSV file
+#'
+#' @param src A character string with path and name of an Innovasea VDAT CSV
+#'   detection file. If only file name is given, then the file must be located
+#'   in the working directory.
+#'
+#' @param record_types An optional vector of character strings with names of
+#'   record types to read from the file. E.g., "DET" for detection records.
+#'   Default (\code{NULL}) will read all record types present in input CSV
+#'   \code{src}.
+#'
+#' @param show_progress Optional argument passed to \code{data.table::fread}'s
+#'   \code{showProgress}.
+#'
+#' @details Reading is done via \code{\link[data.table]{fread}}.
+#'
+#' @details All timestamp columns are assumed to be in UTC and are assigned
+#'   class \code{POSIXct}. The internal value of timestamps will include
+#'   fractional seconds but the printed value (i.e., displayed or written to
+#'   file) will be truncated according to \code{options()$digits.secs}. By
+#'   default (\code{options()$digits.secs = NULL}), values are truncated (i.e.,
+#'   rounded down) to the nearest second. To maintain the full resolution
+#'   present in the input Fathom CSV file, set \code{options(digits.secs = 6)}.
+#'
+#' @return A list of class \code{vdat_list} with one named element for each
+#'   record type and attributes: \code{fathom_csv_format_version} with version
+#'   of the input Fathom CSV format; \code{source} with version of
+#'   VDAT.exe used to create the input file.
+#'
+#'
+#' @author C. Holbrook (cholbrook@@usgs.gov)
+#'
+#' @examples
+#' \dontrun{
+#' # Example 1. Read a single file
+#'
+#' vrl_file <- system.file("extdata", "detection_files_raw",
+#'   "VR2W_109924_20110718_1.vrl",
+#'   package = "glatos"
+#' )
+#'
+#' temp_dir <- tempdir()
+#'
+#' csv_file <- vdat_convert(vrl_file, out_dir = temp_dir)
+#'
+#' # utils::browseURL(temp_dir)
+#'
+#' # read all record types
+#' vdat <- read_vdat_csv(csv_file)
+#'
+#' # read only one record type
+#' vdat <- read_vdat_csv(csv_file, record_types = c("DET"))
+#'
+#'
+#' # Example 2. Read and combine detection records from multiple files
+#'
+#' # get two example files
+#' vrl_files <- system.file("extdata", "detection_files_raw",
+#'   c(
+#'     "VR2W_109924_20110718_1.vrl",
+#'     "HR2-180 461396 2021-04-20 173145.vdat"
+#'   ),
+#'   package = "glatos"
+#' )
+#'
+#' csv_files <- vdat_convert(vrl_files, out_dir = temp_dir)
+#'
+#'
+#' # using dplyr
+#'
+#' library(dplyr)
+#'
+#' # basic steps: import each to list element, subset DET records,
+#' #              add column with source file name, combine.
+#'
+#' det2_tbl <- csv_files %>%
+#'   lapply(
+#'     function(x) {
+#'       read_vdat_csv(x, record_types = "DET")$DET %>%
+#'         as_tibble() %>%
+#'         mutate(source_file = basename(x))
+#'     }
+#'   ) %>%
+#'   bind_rows()
+#'
+#'
+#' # using data.table
+#'
+#' library(data.table)
+#'
+#' det2_dt <- rbindlist(
+#'   lapply(
+#'     csv_files,
+#'     function(x) {
+#'       read_vdat_csv(x, record_types = "DET")$DET[
+#'         ,
+#'         source_file := basename(x)
+#'       ]
+#'     }
+#'   )
+#' )
+#'
+#'
+#' # get current version of digits.secs
+#' op_digits.secs <- options()$digits.secs
+#'
+#' # set digits.secs = NULL (default, truncates to nearest second)
+#' options(digits.secs = NULL)
+#'
+#' # note truncation to nearest second
+#' vdat$DET$Time[2]
+#'
+#' # set digits.secs to see fractional seconds
+#' options(digits.secs = 6)
+#'
+#' # note fractional seconds
+#' vdat$DET$Time[2]
+#'
+#' # return to default values
+#' options(digits.secs = op_digits.secs)
+#'
+#' # or specify via format %OSn e.g., when writing to disk or external database
+#' # see ?strptime
+#' format(vdat$DET$Time[2], format = "%Y-%m-%d %H:%M:%OS6")
+#' }
+#'
+#' @export
+read_vdat_csv <- function(src,
+                          record_types = NULL,
+                          show_progress = FALSE) {
+  # Check if exists
+  if (!file.exists(src)) {
+    warning("File not found: ", src)
+    return()
+  }
+
+  # Identify vdat csv format version and vdat.exe version that created input csv
+  vdat_header <- data.table::fread(file = src, nrows = 1L, header = FALSE)
+
+  # Check if fathom csv format (error if looks like VUE export format)
+  if (vdat_header$V1[1] == "VEMCO DATA LOG") {
+    # Set column names
+    src_version <- data.table::data.table(
+      fathom_csv = vdat_header$V2[1],
+      vdat_exe = vdat_header$V3[1]
+    )
+  } else if (all(c(
+    "Receiver",
+    "Transmitter",
+    "Transmitter Name",
+    "Transmitter Serial",
+    "Sensor Value",
+    "Sensor Unit",
+    "Station Name",
+    "Latitude",
+    "Longitude"
+  ) %in%
+    as.character(vdat_header))) {
+    stop(
+      "Input file appears to be in VUE Export format, which is not ",
+      "supported.\n Only Fathom CSV format is supported. \n",
+      " Perhaps you want read_vue_detection_csv()?"
+    )
+  }
+
+  # Read all data into character vector (like readLines)
+  vdat_txt <- data.table::fread(
+    file = src, skip = 2, header = FALSE,
+    sep = NULL, col.names = "txt",
+    showProgress = show_progress
+  )
+
+  # Identify record type of each row
+  vdat_txt[, record_type := data.table::fread(
+    file = src,
+    skip = 2,
+    header = FALSE,
+    sep = ",",
+    select = 1,
+    fill = TRUE,
+    showProgress = show_progress
+  )]
+
+  # Drop _DESC from headers
+  vdat_txt[, record_type := gsub("_DESC$", "", record_type)]
+
+
+  # Get record identifiers from csv file
+  csv_record_types <- unique(vdat_txt$record_type)
+
+
+  if (is.null(record_types)) {
+    record_types <- csv_record_types
+  } else {
+    # Check if any record_types are not in csv
+    unknown_record_types <- setdiff(record_types, csv_record_types)
+
+    if (length(unknown_record_types) > 0) {
+      stop(
+        "The following input ",
+        "'record_types' ",
+        "were not found in CSV file: \n\t",
+        paste(unknown_record_types,
+          collapse = ", "
+        )
+      )
+    }
+  }
+
+  # Drop data types not requested by user
+  vdat_txt <- vdat_txt[record_type %in% record_types]
+
+  # Split into list elements by record type
+  vdat_list <- split(vdat_txt,
+    by = "record_type",
+    keep.by = FALSE
+  )
+
+  data(vdat_csv_schema)
+
+  vdat_csv_schema <- vdat_csv_schema[[paste0("v", src_version$fathom_csv)]]
+
+
+  # Preallocate list; element = record type
+  vdat <- setNames(
+    object = vector("list", length(vdat_list)),
+    nm = names(vdat_list)
+  )
+
+  for (i in 1:length(vdat)) {
+    # fread has issues with numerical precision (e.g., 'Time Correction (s)')
+    #  so read all columns as character then coerce
+    vdat[[i]] <- data.table::fread(
+      text = paste0(c(vdat_list[[i]]$txt, ""),
+        collapse = "\n"
+      ),
+      sep = ",", na.strings = "",
+      colClasses = "character",
+      header = TRUE,
+      drop = 1,
+      showProgress = show_progress
+    )
+
+    # Coerce to class
+    schema_i <- vdat_csv_schema[[names(vdat[i])]]
+
+    # numeric
+    numeric_cols <- schema_i$name[schema_i$type == "numeric"]
+
+    if (length(numeric_cols) > 0) {
+      vdat[[i]][, (numeric_cols) := lapply(.SD, as.numeric),
+        .SDcols = numeric_cols
+      ]
+    }
+
+    # POSIXct
+    timestamp_cols <- schema_i$name[schema_i$type == "POSIXct"]
+
+    if (length(timestamp_cols) > 0) {
+      vdat[[i]][, (timestamp_cols) := lapply(
+        .SD,
+        function(x) {
+          lubridate::fast_strptime(
+            x,
+            format = "%Y-%m-%d %H:%M:%OS",
+            lt = FALSE
+          )
+        }
+      ),
+      .SDcols = timestamp_cols
+      ]
+    }
+
+    # Assign class
+    new_class <- c(paste0("vdat_", names(vdat[i])), class(vdat[[i]]))
+    data.table::setattr(vdat[[i]], "class", new_class)
+  } # end i
+
+  # Assign class and other attributes
+  vdat_list <- structure(vdat,
+    class = c("vdat_list", class(vdat)),
+    fathom_csv_version = src_version$fathom_csv,
+    source = src_version$vdat_exe
+  )
+
+  return(vdat_list)
+}
diff --git a/R/load-write_vdat_csv.r b/R/load-write_vdat_csv.r
index d804f96c..6ffd201d 100644
--- a/R/load-write_vdat_csv.r
+++ b/R/load-write_vdat_csv.r
@@ -44,6 +44,7 @@
 #' @author C. Holbrook (cholbrook@@usgs.gov)
 #'
 #' @examples
+#' \dontrun{
 #'
 #' # Example 1. Read and write a single file
 #'
@@ -66,7 +67,7 @@
 #'
 #' # write to multiple files
 #' write_vdat_csv(vdat, output_format = "csv.fathom.split")
-#'
+#' }
 #' @export
 write_vdat_csv <- function(vdat,
                            record_types = NULL,
diff --git a/R/sim-calc_collision_prob.r b/R/sim-calc_collision_prob.r
index 93d2538f..158febc6 100644
--- a/R/sim-calc_collision_prob.r
+++ b/R/sim-calc_collision_prob.r
@@ -1,28 +1,28 @@
 #' Estimate probability of collision for telemetry transmitters
-#' 
-#' Estimate (by simulation) probability of collision for co-located telemetry 
-#'   transmitters with pulse-period-modulation type encoding 
 #'
-#' @param delayRng A 2-element numeric vector with minimum and maximum delay 
+#' Estimate (by simulation) probability of collision for co-located telemetry
+#'   transmitters with pulse-period-modulation type encoding
+#'
+#' @param delayRng A 2-element numeric vector with minimum and maximum delay
 #'   (time in seconds from end of one coded burst to beginning of next).
-#'   
-#' @param burstDur A numeric scalar with duration (in seconds) of each coded 
+#'
+#' @param burstDur A numeric scalar with duration (in seconds) of each coded
 #'   burst (i.e., pulse train).
-#'   
-#' @param maxTags A numeric scalar with maximum number of co-located  
+#'
+#' @param maxTags A numeric scalar with maximum number of co-located
 #'   transmitters (within detection range at same time).
-#'   
-#' @param nTrans A numeric scalar with the number of transmissions to simulate 
+#'
+#' @param nTrans A numeric scalar with the number of transmissions to simulate
 #'   for each co-located transmitter.
 #'
 #' @details
 #' Calculates the detection probability associated with collision, given delay
-#' range (delayRng), burst duration (burstDur), maximum number of co-located 
+#' range (delayRng), burst duration (burstDur), maximum number of co-located
 #' tags (maxTags), and number of simulated transmission per tag (nTrans). The
 #' simulation estimates detection probability due only to collisions (i.e., when
-#' no other variables influence detection probability) and assuming that all 
+#' no other variables influence detection probability) and assuming that all
 #' tags are co-located at a receiver for the duration of the simulation.
-#' 
+#'
 #' @return A data frame containing summary statistics:
 #'   \item{nTags}{Number of tags within detection range at one time}
 #'   \item{min}{Minimum detection probability among simulated tags}
@@ -31,12 +31,12 @@
 #'   \item{q3}{Third quartile of detection probabilities among simulated tags}
 #'   \item{max}{Maximum detection probability among simulated tags}
 #'   \item{mean}{Mean detection probability among simulated tags}
-#'   \item{expDetsPerHr}{Expected number of detections per hour assuming 
-#'      perfect detection probability, given the number of tags within 
+#'   \item{expDetsPerHr}{Expected number of detections per hour assuming
+#'      perfect detection probability, given the number of tags within
 #'      detection range}
-#'   \item{totDetsPerHr}{Observed number of detections per hour for a given 
+#'   \item{totDetsPerHr}{Observed number of detections per hour for a given
 #'      number of tags}
-#'   \item{effDelay}{Eeffective delay of the transmitter after incorporating 
+#'   \item{effDelay}{Eeffective delay of the transmitter after incorporating
 #'      collisions}
 #'   \item{detsPerTagPerHr}{Mean number of detections per hour per tag}
 
@@ -50,77 +50,86 @@
 #' @author C. Holbrook (cholbrook@usgs.gov) and T. Binder
 #'
 #' @examples
-#' #parameters analagous to Vemco tag, global coding, 45 s nominal delay
-#' foo <- calc_collision_prob(delayRng = c(45, 90), burstDur = 5.12, maxTags = 50,
-#'     nTrans = 10000)
-#'     
-#' #plot probabilities of detection
-#' plot(med~nTags, data=foo, type='p', pch=20, ylim=c(0,1),
-#'   b
-#'   
-#' #plot probability of collision by subtracting detection probability from 1
-#' plot((1 - med)~nTags, data=foo, type='p', pch=20, ylim=c(0,1),
-#'   xlab="# of transmitters within range", ylab="Probability of collision")
+#' # parameters analagous to Vemco tag, global coding, 45 s nominal delay
+#' foo <- calc_collision_prob(
+#'   delayRng = c(45, 90), burstDur = 5.12, maxTags = 50,
+#'   nTrans = 10000
+#' )
+#'
+#' # plot probabilities of detection
+#' plot(med ~ nTags,
+#'   data = foo, type = "p", pch = 20, ylim = c(0, 1),
+#'   xlab = "# of transmitters within range", ylab = "Probability of detection"
+#' )
+#'
+#' # plot probability of collision by subtracting detection probability from 1
+#' plot((1 - med) ~ nTags,
+#'   data = foo, type = "p", pch = 20, ylim = c(0, 1),
+#'   xlab = "# of transmitters within range", ylab = "Probability of collision"
+#' )
 #'
 #' @export
-calc_collision_prob = function(delayRng = c(60, 180), burstDur = 5.0, 
-  maxTags = 50, nTrans = 10000)
-    {
+calc_collision_prob <- function(
+    delayRng = c(60, 180), burstDur = 5.0,
+    maxTags = 50, nTrans = 10000) {
+  # preallocate objects
 
-	# preallocate objects
-  
   # transmission history (before collisions)
-	pingHist <- vector("list", length = maxTags) 
-	# list of logical value; indicates collision
-	collide <- vector("list", length = maxTags) 
-	# preallocate
-	detProbs <- data.frame(nTags= 1:maxTags, min= NA, q1= NA, med= NA, q3= NA, 
-	  max= NA, mean= NA) 	
-	# observed detection history (after collisions)
-	pingHistObs <- vector("list", length = maxTags) 
+  pingHist <- vector("list", length = maxTags)
+  # list of logical value; indicates collision
+  collide <- vector("list", length = maxTags)
+  # preallocate
+  detProbs <- data.frame(
+    nTags = 1:maxTags, min = NA, q1 = NA, med = NA, q3 = NA,
+    max = NA, mean = NA
+  )
+  # observed detection history (after collisions)
+  pingHistObs <- vector("list", length = maxTags)
 
   # define transmission and detection histories for each tag
-  for (i in 1:maxTags){
-    #create transmission history; each list element is a tag, 
-    # odd ind = start, even ind = end 
-		# draw transmissions from uniform distribution within delayRng
-    #random start time 
-    pingStart <- cumsum(runif(nTrans, delayRng[1], delayRng[2])+burstDur) 
+  for (i in 1:maxTags) {
+    # create transmission history; each list element is a tag,
+    # odd ind = start, even ind = end
+    # draw transmissions from uniform distribution within delayRng
+    # random start time
+    pingStart <- cumsum(runif(nTrans, delayRng[1], delayRng[2]) + burstDur)
     pingHist[[i]] <- sort(c(pingStart, pingStart + burstDur))
-		
-		if(i==1) detProbs[i,] <- c(i, rep(1,6))
-    if(i>1){  # check to see if collided with any previous tag
-      for(j in 1:(i-1)) {
-				#check to see if ith tag transmissions overlaps 
+
+    if (i == 1) detProbs[i, ] <- c(i, rep(1, 6))
+    if (i > 1) { # check to see if collided with any previous tag
+      for (j in 1:(i - 1)) {
+        # check to see if ith tag transmissions overlaps
         # with any jth tag transmissions
-        pingInts <- findInterval(pingHist[[i]], pingHist[[j]]) 
-				
-				#identify collisions (TRUE) or nonCollisions (FALSE)
-        collisions <- (pingInts/2) != floor(pingInts/2) 
-            
-        collide[[j]] <- unique(c(collide[[j]], ceiling(pingInts[collisions]/2)))
-        collide[[i]] <- unique(c(collide[[i]], 
-				ceiling(row(as.matrix(collisions))[collisions]/2)))                  
+        pingInts <- findInterval(pingHist[[i]], pingHist[[j]])
+
+        # identify collisions (TRUE) or nonCollisions (FALSE)
+        collisions <- (pingInts / 2) != floor(pingInts / 2)
+
+        collide[[j]] <- unique(c(collide[[j]], ceiling(pingInts[collisions] / 2)))
+        collide[[i]] <- unique(c(
+          collide[[i]],
+          ceiling(row(as.matrix(collisions))[collisions] / 2)
+        ))
       }
-        
-      detProb.k <- 1 - (sapply(collide[1:i], length)/
-        (sapply(pingHist[1:i], length)/2))
-        
-      detProbs[i,2:7] <- c(fivenum(detProb.k), mean(detProb.k))                       
-    } #end if
-    
-    detProbs <- round(detProbs, 3)  
-  } #end i
-  
+
+      detProb.k <- 1 - (sapply(collide[1:i], length) /
+        (sapply(pingHist[1:i], length) / 2))
+
+      detProbs[i, 2:7] <- c(fivenum(detProb.k), mean(detProb.k))
+    } # end if
+
+    detProbs <- round(detProbs, 3)
+  } # end i
+
   # calculate total number of hourly detects across all fish
   nomDelay <- median(delayRng) # nominal delay
-  #expected detects per tag per hour
-  expDetsPerTagPerHr <- (3600/(nomDelay + burstDur)) 
+  # expected detects per tag per hour
+  expDetsPerTagPerHr <- (3600 / (nomDelay + burstDur))
 
-  detProbs$expDetsPerHr <- expDetsPerTagPerHr*detProbs$nTags
-  detProbs$totDetsPerHr <- round(with(detProbs, expDetsPerHr*mean), 0)
-  detProbs$effDelay <- round(with(detProbs, nomDelay*(1/mean)), 0) 
-  detProbs$detsPerTagPerHr <- round(with(detProbs, totDetsPerHr/nTags))
+  detProbs$expDetsPerHr <- expDetsPerTagPerHr * detProbs$nTags
+  detProbs$totDetsPerHr <- round(with(detProbs, expDetsPerHr * mean), 0)
+  detProbs$effDelay <- round(with(detProbs, nomDelay * (1 / mean)), 0)
+  detProbs$detsPerTagPerHr <- round(with(detProbs, totDetsPerHr / nTags))
 
   return(detProbs)
 }
diff --git a/R/sim-receiver_line_det_sim.r b/R/sim-receiver_line_det_sim.r
index 81d93506..aba4638a 100644
--- a/R/sim-receiver_line_det_sim.r
+++ b/R/sim-receiver_line_det_sim.r
@@ -93,20 +93,20 @@
 #' dp
 #'
 #' # Again with only 10 virtual fish and optional plot to see simulated data
-#' dp <- receiver_line_det_sim(rngFun = pdrf, nsim = 10, showPlot = T) # w/ optional plot
+#' dp <- receiver_line_det_sim(rngFun = pdrf, nsim = 10, showPlot = TRUE) # w/ optional plot
 #' dp
 #'
 #' # Again but six receivers and allow fish to pass to left and right of line
 #' dp <- receiver_line_det_sim(
 #'   rngFun = pdrf, recSpc = rep(1000, 5),
-#'   outerLim = c(1000, 1000), nsim = 10, showPlot = T
+#'   outerLim = c(1000, 1000), nsim = 10, showPlot = TRUE
 #' )
 #' dp
 #'
 #' # Again but four receivers with irregular spacing
 #' dp <- receiver_line_det_sim(
 #'   rngFun = pdrf, recSpc = c(2000, 4000, 2000),
-#'   outerLim = c(1000, 1000), nsim = 10, showPlot = T
+#'   outerLim = c(1000, 1000), nsim = 10, showPlot = TRUE
 #' )
 #' dp
 #'
@@ -173,7 +173,7 @@
 #' )
 #'
 #' # use empirical curve (edrf) in simulation
-#' dp <- receiver_line_det_sim(rngFun = edrf, nsim = 10, showPlot = T) # w/ optional plot
+#' dp <- receiver_line_det_sim(rngFun = edrf, nsim = 10, showPlot = TRUE) # w/ optional plot
 #' dp
 #'
 #' @export
diff --git a/R/summ-receiver_efficiency.r b/R/summ-receiver_efficiency.r
index 9a4f3036..aafd20df 100644
--- a/R/summ-receiver_efficiency.r
+++ b/R/summ-receiver_efficiency.r
@@ -35,6 +35,7 @@
 #' @author Alex Nunes  \email{anunes@dal.ca}
 #'
 #' @examples
+#' \dontrun{
 #' det_file <- system.file("extdata", "hfx_detections.csv",
 #'   package = "glatos"
 #' )
@@ -47,7 +48,7 @@
 #' dets <- glatos::read_otn_detections(det_file)
 #'
 #' hfx_receiver_efficiency_index <- glatos::REI(dets, hfx_deployments)
-#'
+#' }
 #' @importFrom dplyr group_by mutate coalesce filter summarise
 #' @importFrom magrittr "%>%"
 #'
diff --git a/R/summ-residence_index.r b/R/summ-residence_index.r
index 0085fa1a..2b5959a5 100644
--- a/R/summ-residence_index.r
+++ b/R/summ-residence_index.r
@@ -437,15 +437,15 @@ get_days <- function(dets, calculation_method = "kessel",
                      time_interval_size = "1 day") {
   days <- 0
   if (calculation_method == "aggregate_with_overlap") {
-    days <- glatos:::aggregate_total_with_overlap(dets)
+    days <- aggregate_total_with_overlap(dets)
   } else if (calculation_method == "aggregate_no_overlap") {
-    days <- glatos:::aggregate_total_no_overlap(dets)
+    days <- aggregate_total_no_overlap(dets)
   } else if (calculation_method == "timedelta") {
-    days <- glatos:::total_diff_days(dets)
+    days <- total_diff_days(dets)
   } else if (calculation_method == "kessel") {
-    days <- glatos:::interval_count(dets, time_interval_size = "1 day")
+    days <- interval_count(dets, time_interval_size = "1 day")
   } else if (calculation_method == "time_interval") {
-    days <- glatos:::interval_count(dets, time_interval_size)
+    days <- interval_count(dets, time_interval_size)
   } else {
     stop("Unsupported 'calculated_method'.")
   }
diff --git a/R/util-convert_otn_to_att.r b/R/util-convert_otn_to_att.r
index c38b1233..e104ce15 100644
--- a/R/util-convert_otn_to_att.r
+++ b/R/util-convert_otn_to_att.r
@@ -38,7 +38,7 @@
 #'   and station metadata, to be ingested by VTrack/ATT
 #'
 #' @examples
-#'
+#' \dontrun{
 #' #--------------------------------------------------
 #' # EXAMPLE #1 - loading from Deployment Object
 #'
@@ -59,7 +59,7 @@
 #' deploy <- read_otn_deployments(deploy_path)
 #'
 #' ATTdata <- convert_otn_to_att(dets, tags, deploymentObj = deploy)
-#'
+#' }
 #' #--------------------------------------------------
 #' # EXAMPLE #2 - loading from Deployment Sheet
 #'
diff --git a/R/util-kml_to_csv.r b/R/util-kml_to_csv.r
index f3b39dba..01971d40 100644
--- a/R/util-kml_to_csv.r
+++ b/R/util-kml_to_csv.r
@@ -26,7 +26,7 @@
 #' @examples
 #'
 #' # Get example kml with two polygons
-#' kml_file <- system.file("inst/extdata", "example_polygons.kml",
+#' kml_file <- system.file("extdata", "example_polygons.kml",
 #'   package = "glatos"
 #' )
 #'
diff --git a/R/util-vrl2csv.r b/R/util-vrl2csv.r
index bd26d027..6c17872f 100644
--- a/R/util-vrl2csv.r
+++ b/R/util-vrl2csv.r
@@ -65,7 +65,7 @@
 #' vrl2csv(myVRL) # file name input
 #'
 #' # setting 'overwrite=FALSE' will make new file with '_n'added to name
-#' vrl2csv(myVRL, overwrite = F)
+#' vrl2csv(myVRL, overwrite = FALSE)
 #' }
 #'
 #' @name vrl2csv-deprecated
diff --git a/R/vis-make_transition3.r b/R/vis-make_transition3.r
index 42108190..14f8cef7 100644
--- a/R/vis-make_transition3.r
+++ b/R/vis-make_transition3.r
@@ -1,154 +1,156 @@
-##' Create transition layer from polygon shapefile
-##'
-##' Create transition layer for [interpolate_path] from polygon shapefile.
-##'
-##' @param poly A spatial polygon object of class
-##'   [SpatialPolygonsDataFrame][SpatialPolygons] or a
-##'   [sf::sf()][sf::sf] object with a geometry column of polygon
-##'   and/or multipolygon objects.
-##'
-##' @param res two element vector that specifies the x and y dimension
-##'   of output raster cells.  Units of res are same as input
-##'   shapefile.
-##'
-##' @param receiver_points A SpatialPointsDataFrame object that
-##'   contains coordinates of receivers  dataset or a "glatos_receivers" object.
-##'
-##' @param epsg coordinate reference code that describes projection
-##'   used for map calculation and rasterization.  Defaults to
-##'   NAD83/Great Lakes and St. Lawrence Albers.
-##'
-##' @details `make_transition` uses [fasterize][fasterize::fasterize]
-##'   to convert a polygon shapefile into a raster layer and
-##'   geo-corrected transition layer [interpolate_path].  Raster
-##'   cell values on land equal 1 cells in water equal 0. Output is a
-##'   two-object list containing the raster layer and transition
-##'   layer.  Both objects have the same extents and geographic
-##'   projection as input shapefile.
-##'
-##'  @details If receiver\_points is provided, any receiver not in water
-##'   is buffered by the distance from the receiver to the nearest
-##'   water.  This allows all receivers to be coded as in water if the
-##'   receiver is on land.
-##'
-##' @details Poly object is transformed into planer map projection
-##'   specified by epsg argument for calculation of transition object
-##'   if receiver_points is provided.  Output is projected to WGS84
-##'   (epsg- 4326).
-##'
-##' @details output transition layer is corrected for projection
-##'   distortions using `gdistance::geoCorrection`.  Adjacent
-##'   cells are connected by 16 directions and transition function
-##'   returns 0 (land) for movements between land and water and 1 for
-##'   all over-water movements.
-##'
-##'
-##' @return A list with two elements:
-##' \describe{
-##'    \item{transition}{a geo-corrected transition raster layer where land = 0
-##'       and water=1
-##'   (see `gdistance`)}
-##'    \item{rast}{rasterized input layer of class `raster`}}
-##'   Additonally, rasterized version of input shapefile (*.tif extension) is written to computer
-##'   at `output_dir`
-##'
-##'
-##' @author Todd Hayden, Tom Binder, Chris Holbrook
-##'
-##' @examples
-##'
-##' #Example 1 - read from SpatialPolygonsDataFrame
-##' # use example polygon for Great lakes
-##'
-##' #get polygon of the Great Lakes
-##' data(great_lakes_polygon) #glatos example data; a sf polygon object
-##'
-##' # make_transition layer
-##' tst <- make_transition3(great_lakes_polygon, res = c(0.1, 0.1))
-##'
-##' # plot raster layer
-##' # notice land = 1, water = 0
-##' plot(terra::rast(tst$rast))
-##'
-##' #compare to polygon
-##' plot(st_geometry(great_lakes_polygon), add = TRUE, fill = NA)
-##'
-##' #Example 2 - read from ESRI Shapefile and include receiver file
-##' # to account for any receivers outside of great lakes polygon
-##'
-##' # path to polygon shapefile
-##' poly <- system.file("extdata", "shoreline.zip", package = "glatos")
-##' poly <- unzip(poly, exdir = tempdir())
-##' poly <- sf::st_read(poly[grepl("*.shp", poly)])
-##'
-##' # read in glatos receivers object
-##' rec_file <- system.file("extdata", "sample_receivers.csv", package="glatos")
-##' recs <- read_glatos_receivers(rec_file)
-##'
-##' # change a coordinate to on-land to show impact...
-##' recs[1, "deploy_lat"] <- recs[1,"deploy_lat"]+4
-##'
-##' # make_transition layer
-##' tst <- make_transition3(poly, res = c(0.1, 0.1), receiver_points = recs)
-##'
-##' # plot raster layer
-##' # notice the huge circle rasterized as "water"  north of Lake Superior.
-##' # This occurred because we had a "receiver" deployed at that locations
-##' plot(terra::rast(tst$rast))
-##' points(recs$deploy_long, recs$deploy_lat, col = "red", pch = 20)
-##'
-##' # plot transition layer
-##' plot(raster::raster(tst$transition))
-##'
-##' Example 3- transition layer of Lake Huron only with receivers
-##'
-##' # read polygon layer of Great Lakes
-##' data(great_lakes_polygon)
-##'
-##' # transform to great lakes projection
-##' poly <- sf::st_transform(great_lakes_polygon, crs = 3175)
-##'
-##' # set attribute-geometry relationship to constant.
-##' # this avoids error when cropping
-##' sf::st_agr(poly) = "constant"
-##'
-##' # crop Great lakes polygon file
-##' poly <- sf::st_crop(x = poly, xmin = 829242.55, ymin = 698928.27,
-##'                               xmax = 1270000.97, ymax = 1097196.15)
-##'
-##' # read in glatos receivers object
-##' rec_file <- system.file("extdata", "sample_receivers.csv", package="glatos")
-##' recs <- read_glatos_receivers(rec_file)
-##'
-##' # extract receivers in "HECWL" project
-##' # all receiver stations except one is in Lake Huron
-##' recs <- recs[recs$glatos_project == "HECWL",]
-##'
-##' # remove two stations not in Lake Huron
-##' recs <- recs[!recs$glatos_array %in% c("MAU","LVD"),]
-##'
-##' # convert recs to simple feature  object (sf)
-##' recs <- sf::st_as_sf(recs, coords = c("deploy_long", "deploy_lat"), crs = 4326 )
-##'
-##' # transform receivers to same projection as great lakes polygon
-##' recs <- sf::st_transform(recs, crs = 3175)
-##'
-##' # check by plotting
-##' plot(sf::st_geometry(poly), col = NA)
-##' plot(sf::st_geometry(recs), col = "red", add = TRUE)
-##'
-##' # create slightly higher resolution transition layer
-##' tst1 <- make_transition3(poly, res = c(0.01, 0.01), receiver_points = recs)
-##'
-##' # plot raster layer
-##' plot(terra::rast(tst1$rast))
-##' plot(sf::st_transform(sf::st_geometry(recs), crs = 4326), add = TRUE, col = "red", pch = 20)
-##'
-##' # plot transition layer
-##' raster::plot(raster::raster(tst1$transition))
-##'
-##'
-##' @export
+#' Create transition layer from polygon shapefile
+#'
+#' Create transition layer for [interpolate_path] from polygon shapefile.
+#'
+#' @param poly A spatial polygon object of class
+#'   [SpatialPolygonsDataFrame][SpatialPolygons] or a
+#'   [sf::sf()][sf::sf] object with a geometry column of polygon
+#'   and/or multipolygon objects.
+#'
+#' @param res two element vector that specifies the x and y dimension
+#'   of output raster cells.  Units of res are same as input
+#'   shapefile.
+#'
+#' @param receiver_points A SpatialPointsDataFrame object that
+#'   contains coordinates of receivers  dataset or a "glatos_receivers" object.
+#'
+#' @param epsg coordinate reference code that describes projection
+#'   used for map calculation and rasterization.  Defaults to
+#'   NAD83/Great Lakes and St. Lawrence Albers.
+#'
+#' @details `make_transition` uses [fasterize][fasterize::fasterize]
+#'   to convert a polygon shapefile into a raster layer and
+#'   geo-corrected transition layer [interpolate_path].  Raster
+#'   cell values on land equal 1 cells in water equal 0. Output is a
+#'   two-object list containing the raster layer and transition
+#'   layer.  Both objects have the same extents and geographic
+#'   projection as input shapefile.
+#'
+#'  @details If receiver\_points is provided, any receiver not in water
+#'   is buffered by the distance from the receiver to the nearest
+#'   water.  This allows all receivers to be coded as in water if the
+#'   receiver is on land.
+#'
+#' @details Poly object is transformed into planer map projection
+#'   specified by epsg argument for calculation of transition object
+#'   if receiver_points is provided.  Output is projected to WGS84
+#'   (epsg- 4326).
+#'
+#' @details output transition layer is corrected for projection
+#'   distortions using `gdistance::geoCorrection`.  Adjacent
+#'   cells are connected by 16 directions and transition function
+#'   returns 0 (land) for movements between land and water and 1 for
+#'   all over-water movements.
+#'
+#'
+#' @return A list with two elements:
+#' \describe{
+#'    \item{transition}{a geo-corrected transition raster layer where land = 0
+#'       and water=1
+#'   (see `gdistance`)}
+#'    \item{rast}{rasterized input layer of class `raster`}}
+#'   Additonally, rasterized version of input shapefile (*.tif extension) is written to computer
+#'   at `output_dir`
+#'
+#'
+#' @author Todd Hayden, Tom Binder, Chris Holbrook
+#'
+#' @examples
+#'
+#' # Example 1 - read from SpatialPolygonsDataFrame
+#' # use example polygon for Great lakes
+#'
+#' # get polygon of the Great Lakes
+#' data(great_lakes_polygon) # glatos example data; a sf polygon object
+#'
+#' # make_transition layer
+#' tst <- make_transition3(great_lakes_polygon, res = c(0.1, 0.1))
+#'
+#' # plot raster layer
+#' # notice land = 1, water = 0
+#' raster::plot(tst$rast)
+#'
+#' # compare to polygon
+#' plot(sf::st_geometry(great_lakes_polygon), add = TRUE, fill = NA)
+#'
+#' # Example 2 - read from ESRI Shapefile and include receiver file
+#' # to account for any receivers outside of great lakes polygon
+#'
+#' # path to polygon shapefile
+#' poly <- system.file("extdata", "shoreline.zip", package = "glatos")
+#' poly <- sf::st_read(paste0("/vsizip/", poly))
+#'
+#' # read in glatos receivers object
+#' rec_file <- system.file("extdata", "sample_receivers.csv", package = "glatos")
+#' recs <- read_glatos_receivers(rec_file)
+#'
+#' # change a coordinate to on-land to show impact...
+#' recs[1, "deploy_lat"] <- recs[1, "deploy_lat"] + 4
+#'
+#' # make_transition layer
+#' tst <- make_transition3(poly, res = c(0.1, 0.1), receiver_points = recs)
+#'
+#' # plot raster layer
+#' # notice the huge circle rasterized as "water"  north of Lake Superior.
+#' # This occurred because we had a "receiver" deployed at that locations
+#' raster::plot(tst$rast)
+#' points(recs$deploy_long, recs$deploy_lat, col = "red", pch = 20)
+#'
+#' # plot transition layer
+#' raster::plot(raster::raster(tst$transition))
+#'
+#' # Example 3- transition layer of Lake Huron only with receivers
+#'
+#' # read polygon layer of Great Lakes
+#' data(great_lakes_polygon)
+#'
+#' # transform to great lakes projection
+#' poly <- sf::st_transform(great_lakes_polygon, crs = 3175)
+#'
+#' # set attribute-geometry relationship to constant.
+#' # this avoids error when cropping
+#' sf::st_agr(poly) <- "constant"
+#'
+#' # crop Great lakes polygon file
+#' poly <- sf::st_crop(
+#'   x = poly, xmin = 829242.55, ymin = 698928.27,
+#'   xmax = 1270000.97, ymax = 1097196.15
+#' )
+#'
+#' # read in glatos receivers object
+#' rec_file <- system.file("extdata", "sample_receivers.csv", package = "glatos")
+#' recs <- read_glatos_receivers(rec_file)
+#'
+#' # extract receivers in "HECWL" project
+#' # all receiver stations except one is in Lake Huron
+#' recs <- recs[recs$glatos_project == "HECWL", ]
+#'
+#' # remove two stations not in Lake Huron
+#' recs <- recs[!recs$glatos_array %in% c("MAU", "LVD"), ]
+#'
+#' # convert recs to simple feature  object (sf)
+#' recs <- sf::st_as_sf(recs, coords = c("deploy_long", "deploy_lat"), crs = 4326)
+#'
+#' # transform receivers to same projection as great lakes polygon
+#' recs <- sf::st_transform(recs, crs = 3175)
+#'
+#' # check by plotting
+#' plot(sf::st_geometry(poly), col = NA)
+#' plot(sf::st_geometry(recs), col = "red", add = TRUE)
+#'
+#' # create slightly higher resolution transition layer
+#' tst1 <- make_transition3(poly, res = c(0.01, 0.01), receiver_points = recs)
+#'
+#' # plot raster layer
+#' raster::plot(tst1$rast)
+#' plot(sf::st_transform(sf::st_geometry(recs), crs = 4326),
+#'   add = TRUE, col = "red", pch = 20
+#' )
+#'
+#' # plot transition layer
+#' raster::plot(raster::raster(tst1$transition))
+#'
+#' @export
 
 make_transition3 <- function(poly, res = c(0.1, 0.1), receiver_points = NULL, epsg = 3175) {
   message("Making transition layer...")
diff --git a/man/REI.Rd b/man/REI.Rd
index 77c5bd22..a4ff9312 100644
--- a/man/REI.Rd
+++ b/man/REI.Rd
@@ -41,6 +41,7 @@ REI = (Tr/Ta) x (Sr/Sa) x (DDr/DDa) x (Da/Dr)
 }
 }
 \examples{
+\dontrun{
 det_file <- system.file("extdata", "hfx_detections.csv",
   package = "glatos"
 )
@@ -53,7 +54,7 @@ hfx_deployments <- glatos::read_otn_deployments(dep_file)
 dets <- glatos::read_otn_detections(det_file)
 
 hfx_receiver_efficiency_index <- glatos::REI(dets, hfx_deployments)
-
+}
 }
 \author{
 Alex Nunes  \email{anunes@dal.ca}
diff --git a/man/calc_collision_prob.Rd b/man/calc_collision_prob.Rd
index 75059668..9720a91a 100644
--- a/man/calc_collision_prob.Rd
+++ b/man/calc_collision_prob.Rd
@@ -55,17 +55,23 @@ no other variables influence detection probability) and assuming that all
 tags are co-located at a receiver for the duration of the simulation.
 }
 \examples{
-#parameters analagous to Vemco tag, global coding, 45 s nominal delay
-foo <- calc_collision_prob(delayRng = c(45, 90), burstDur = 5.12, maxTags = 50,
-    nTrans = 10000)
-    
-#plot probabilities of detection
-plot(med~nTags, data=foo, type='p', pch=20, ylim=c(0,1),
-  b
-  
-#plot probability of collision by subtracting detection probability from 1
-plot((1 - med)~nTags, data=foo, type='p', pch=20, ylim=c(0,1),
-  xlab="# of transmitters within range", ylab="Probability of collision")
+# parameters analagous to Vemco tag, global coding, 45 s nominal delay
+foo <- calc_collision_prob(
+  delayRng = c(45, 90), burstDur = 5.12, maxTags = 50,
+  nTrans = 10000
+)
+
+# plot probabilities of detection
+plot(med ~ nTags,
+  data = foo, type = "p", pch = 20, ylim = c(0, 1),
+  xlab = "# of transmitters within range", ylab = "Probability of detection"
+)
+
+# plot probability of collision by subtracting detection probability from 1
+plot((1 - med) ~ nTags,
+  data = foo, type = "p", pch = 20, ylim = c(0, 1),
+  xlab = "# of transmitters within range", ylab = "Probability of collision"
+)
 
 }
 \references{
diff --git a/man/convert_otn_to_att.Rd b/man/convert_otn_to_att.Rd
index 0d3a0bf8..f581bbf4 100644
--- a/man/convert_otn_to_att.Rd
+++ b/man/convert_otn_to_att.Rd
@@ -11,7 +11,7 @@ convert_otn_to_att(
   deploymentObj = NULL,
   deploymentSheet = NULL,
   timeFilter = TRUE,
-  crs = sf::st_crs(3426)
+  crs = sf::st_crs(4326)
 )
 }
 \arguments{
@@ -52,7 +52,7 @@ are found here: https://github.com/ocean-tracking-network/glatos/issues/75#issue
 in a comment by Ryan Gosse.
 }
 \examples{
-
+\dontrun{
 #--------------------------------------------------
 # EXAMPLE #1 - loading from Deployment Object
 
@@ -73,7 +73,7 @@ tags <- prepare_tag_sheet(tag_path, 5, 2)
 deploy <- read_otn_deployments(deploy_path)
 
 ATTdata <- convert_otn_to_att(dets, tags, deploymentObj = deploy)
-
+}
 #--------------------------------------------------
 # EXAMPLE #2 - loading from Deployment Sheet
 
diff --git a/man/glatos_animals.Rd b/man/glatos_animals.Rd
index 207f92eb..36401af8 100644
--- a/man/glatos_animals.Rd
+++ b/man/glatos_animals.Rd
@@ -108,6 +108,8 @@ x_tbl <- as_tibble(x)
 ga_tbl <- as_glatos_animals(x_tbl)
 
 
+# All below will error as invalid
+
 # data.frame input; missing column name
 library(dplyr) # for rename
 x2 <- rename(x,
@@ -115,17 +117,19 @@ x2 <- rename(x,
   release_timestamp = utc_release_date_time
 )
 
-ga2 <- as_glatos_animals(x2)
-
+try(
+  ga2 <- as_glatos_animals(x2)
+)
 
-# data.grame input; wrong column class
+# data.frame input; wrong column class
 x3 <- mutate(x,
   animal_id = as.integer(animal_id),
   utc_release_date_time = as.character(utc_release_date_time)
 )
 
-ga3 <- as_glatos_animals(x3)
-
+try(
+  ga3 <- as_glatos_animals(x3)
+)
 
 # Validation and checking
 
diff --git a/man/glatos_detections.Rd b/man/glatos_detections.Rd
index edbf8d43..c389708a 100644
--- a/man/glatos_detections.Rd
+++ b/man/glatos_detections.Rd
@@ -110,6 +110,8 @@ x_tbl <- as_tibble(x)
 gd_tbl <- as_glatos_detections(x_tbl)
 
 
+# All below will error as invalid
+
 # data.frame input; missing column name
 library(dplyr) # for rename
 x2 <- rename(x,
@@ -117,8 +119,9 @@ x2 <- rename(x,
   det_date_time = detection_timestamp_utc
 )
 
-gd2 <- as_glatos_detections(x2)
-
+try(
+  gd2 <- as_glatos_detections(x2)
+)
 
 # data.frame input; wrong column class
 x3 <- mutate(x,
@@ -126,8 +129,9 @@ x3 <- mutate(x,
   detection_timestamp_utc = as.character(detection_timestamp_utc)
 )
 
-gr3 <- as_glatos_detections(x3)
-
+try(
+  gr3 <- as_glatos_detections(x3)
+)
 
 # Validation and checking
 
diff --git a/man/glatos_receivers.Rd b/man/glatos_receivers.Rd
index 70cbe91e..1d533a59 100644
--- a/man/glatos_receivers.Rd
+++ b/man/glatos_receivers.Rd
@@ -123,6 +123,7 @@ x_tbl <- as_tibble(x)
 
 gr_tbl <- as_glatos_receivers(x_tbl)
 
+# All below will error as invalid
 
 # data.frame input; missing column name
 library(dplyr) # for rename
@@ -131,8 +132,9 @@ x2 <- rename(x,
   deploy_timestamp = deploy_date_time
 )
 
-gr2 <- as_glatos_receivers(x2)
-
+try(
+  gr2 <- as_glatos_receivers(x2)
+)
 
 # data.frame input; wrong column class
 x3 <- mutate(x,
@@ -140,8 +142,9 @@ x3 <- mutate(x,
   deploy_date_time = as.character(deploy_date_time)
 )
 
-gr3 <- as_glatos_receivers(x3)
-
+try(
+  gr3 <- as_glatos_receivers(x3)
+)
 
 # Validation and checking
 
diff --git a/man/kml_to_csv.Rd b/man/kml_to_csv.Rd
index b2f470aa..80c64206 100644
--- a/man/kml_to_csv.Rd
+++ b/man/kml_to_csv.Rd
@@ -35,7 +35,7 @@ saved as kml. Or extract (unzip) kml from kmz.
 \examples{
 
 # Get example kml with two polygons
-kml_file <- system.file("inst/extdata", "example_polygons.kml",
+kml_file <- system.file("extdata", "example_polygons.kml",
   package = "glatos"
 )
 
diff --git a/man/make_transition3.Rd b/man/make_transition3.Rd
index cd31ce4a..24d8dfbd 100644
--- a/man/make_transition3.Rd
+++ b/man/make_transition3.Rd
@@ -63,36 +63,35 @@ all over-water movements.
 }
 \examples{
 
-#Example 1 - read from SpatialPolygonsDataFrame
+# Example 1 - read from SpatialPolygonsDataFrame
 # use example polygon for Great lakes
 
-#get polygon of the Great Lakes
-data(great_lakes_polygon) #glatos example data; a sf polygon object
+# get polygon of the Great Lakes
+data(great_lakes_polygon) # glatos example data; a sf polygon object
 
 # make_transition layer
 tst <- make_transition3(great_lakes_polygon, res = c(0.1, 0.1))
 
 # plot raster layer
 # notice land = 1, water = 0
-plot(terra::rast(tst$rast))
+raster::plot(tst$rast)
 
-#compare to polygon
-plot(st_geometry(great_lakes_polygon), add = TRUE, fill = NA)
+# compare to polygon
+plot(sf::st_geometry(great_lakes_polygon), add = TRUE, fill = NA)
 
-#Example 2 - read from ESRI Shapefile and include receiver file
+# Example 2 - read from ESRI Shapefile and include receiver file
 # to account for any receivers outside of great lakes polygon
 
 # path to polygon shapefile
 poly <- system.file("extdata", "shoreline.zip", package = "glatos")
-poly <- unzip(poly, exdir = tempdir())
-poly <- sf::st_read(poly[grepl("*.shp", poly)])
+poly <- sf::st_read(paste0("/vsizip/", poly))
 
 # read in glatos receivers object
-rec_file <- system.file("extdata", "sample_receivers.csv", package="glatos")
+rec_file <- system.file("extdata", "sample_receivers.csv", package = "glatos")
 recs <- read_glatos_receivers(rec_file)
 
 # change a coordinate to on-land to show impact...
-recs[1, "deploy_lat"] <- recs[1,"deploy_lat"]+4
+recs[1, "deploy_lat"] <- recs[1, "deploy_lat"] + 4
 
 # make_transition layer
 tst <- make_transition3(poly, res = c(0.1, 0.1), receiver_points = recs)
@@ -100,13 +99,13 @@ tst <- make_transition3(poly, res = c(0.1, 0.1), receiver_points = recs)
 # plot raster layer
 # notice the huge circle rasterized as "water"  north of Lake Superior.
 # This occurred because we had a "receiver" deployed at that locations
-plot(terra::rast(tst$rast))
+raster::plot(tst$rast)
 points(recs$deploy_long, recs$deploy_lat, col = "red", pch = 20)
 
 # plot transition layer
-plot(raster::raster(tst$transition))
+raster::plot(raster::raster(tst$transition))
 
-Example 3- transition layer of Lake Huron only with receivers
+# Example 3- transition layer of Lake Huron only with receivers
 
 # read polygon layer of Great Lakes
 data(great_lakes_polygon)
@@ -116,25 +115,27 @@ poly <- sf::st_transform(great_lakes_polygon, crs = 3175)
 
 # set attribute-geometry relationship to constant.
 # this avoids error when cropping
-sf::st_agr(poly) = "constant"
+sf::st_agr(poly) <- "constant"
 
 # crop Great lakes polygon file
-poly <- sf::st_crop(x = poly, xmin = 829242.55, ymin = 698928.27,
-                              xmax = 1270000.97, ymax = 1097196.15)
+poly <- sf::st_crop(
+  x = poly, xmin = 829242.55, ymin = 698928.27,
+  xmax = 1270000.97, ymax = 1097196.15
+)
 
 # read in glatos receivers object
-rec_file <- system.file("extdata", "sample_receivers.csv", package="glatos")
+rec_file <- system.file("extdata", "sample_receivers.csv", package = "glatos")
 recs <- read_glatos_receivers(rec_file)
 
 # extract receivers in "HECWL" project
 # all receiver stations except one is in Lake Huron
-recs <- recs[recs$glatos_project == "HECWL",]
+recs <- recs[recs$glatos_project == "HECWL", ]
 
 # remove two stations not in Lake Huron
-recs <- recs[!recs$glatos_array \%in\% c("MAU","LVD"),]
+recs <- recs[!recs$glatos_array \%in\% c("MAU", "LVD"), ]
 
 # convert recs to simple feature  object (sf)
-recs <- sf::st_as_sf(recs, coords = c("deploy_long", "deploy_lat"), crs = 4326 )
+recs <- sf::st_as_sf(recs, coords = c("deploy_long", "deploy_lat"), crs = 4326)
 
 # transform receivers to same projection as great lakes polygon
 recs <- sf::st_transform(recs, crs = 3175)
@@ -147,13 +148,14 @@ plot(sf::st_geometry(recs), col = "red", add = TRUE)
 tst1 <- make_transition3(poly, res = c(0.01, 0.01), receiver_points = recs)
 
 # plot raster layer
-plot(terra::rast(tst1$rast))
-plot(sf::st_transform(sf::st_geometry(recs), crs = 4326), add = TRUE, col = "red", pch = 20)
+raster::plot(tst1$rast)
+plot(sf::st_transform(sf::st_geometry(recs), crs = 4326),
+  add = TRUE, col = "red", pch = 20
+)
 
 # plot transition layer
 raster::plot(raster::raster(tst1$transition))
 
-
 }
 \author{
 Todd Hayden, Tom Binder, Chris Holbrook
diff --git a/man/read_otn_deployments.Rd b/man/read_otn_deployments.Rd
index aa9c88b6..ead0dbfb 100644
--- a/man/read_otn_deployments.Rd
+++ b/man/read_otn_deployments.Rd
@@ -42,11 +42,13 @@ Column names are changed to match GLATOS standard columns when possible.
 Otherwise, OTN columns and column names are retained.
 }
 \examples{
+\dontrun{
 # get path to example deployments file
 deployment_file <- system.file("extdata", "hfx_deployments.csv",
   package = "glatos"
 )
 dep <- read_otn_deployments(deployment_file)
+}
 
 }
 \author{
diff --git a/man/read_vdat_csv.Rd b/man/read_vdat_csv.Rd
index 4b15428d..d8a681dd 100644
--- a/man/read_vdat_csv.Rd
+++ b/man/read_vdat_csv.Rd
@@ -40,32 +40,37 @@ rounded down) to the nearest second. To maintain the full resolution
 present in the input Fathom CSV file, set \code{options(digits.secs = 6)}.
 }
 \examples{
-
+\dontrun{
 # Example 1. Read a single file
 
-vrl_file <- system.file("extdata", "detection_files_raw", 
-  "VR2W_109924_20110718_1.vrl", package="glatos")
+vrl_file <- system.file("extdata", "detection_files_raw",
+  "VR2W_109924_20110718_1.vrl",
+  package = "glatos"
+)
 
 temp_dir <- tempdir()
 
 csv_file <- vdat_convert(vrl_file, out_dir = temp_dir)
 
-#utils::browseURL(temp_dir)
+# utils::browseURL(temp_dir)
 
-#read all record types
+# read all record types
 vdat <- read_vdat_csv(csv_file)
 
-#read only one record type
+# read only one record type
 vdat <- read_vdat_csv(csv_file, record_types = c("DET"))
- 
+
 
 # Example 2. Read and combine detection records from multiple files
 
 # get two example files
 vrl_files <- system.file("extdata", "detection_files_raw",
-  c("VR2W_109924_20110718_1.vrl", 
-    "HR2-180 461396 2021-04-20 173145.vdat"), 
-  package="glatos")
+  c(
+    "VR2W_109924_20110718_1.vrl",
+    "HR2-180 461396 2021-04-20 173145.vdat"
+  ),
+  package = "glatos"
+)
 
 csv_files <- vdat_convert(vrl_files, out_dir = temp_dir)
 
@@ -74,35 +79,40 @@ csv_files <- vdat_convert(vrl_files, out_dir = temp_dir)
 
 library(dplyr)
 
-# basic steps: import each to list element, subset DET records, 
-               add column with source file name, combine.
-               
-det2_tbl <- csv_files \%>\% 
-             lapply(
-               function(x) {
-                 read_vdat_csv(x, record_types = "DET")$DET \%>\% 
-                 as_tibble \%>\%
-                 mutate(source_file = basename(x))
-               }) \%>\% 
-             bind_rows
+# basic steps: import each to list element, subset DET records,
+#              add column with source file name, combine.
+
+det2_tbl <- csv_files \%>\%
+  lapply(
+    function(x) {
+      read_vdat_csv(x, record_types = "DET")$DET \%>\%
+        as_tibble() \%>\%
+        mutate(source_file = basename(x))
+    }
+  ) \%>\%
+  bind_rows()
 
 
-#using data.table
+# using data.table
 
 library(data.table)
 
 det2_dt <- rbindlist(
-             lapply(csv_files, 
-               function(x) {
-                read_vdat_csv(x, record_types = "DET")$DET[, 
-                  source_file := basename(x)]
-             }))
-             
-\dontrun{
+  lapply(
+    csv_files,
+    function(x) {
+      read_vdat_csv(x, record_types = "DET")$DET[
+        ,
+        source_file := basename(x)
+      ]
+    }
+  )
+)
+
 
 # get current version of digits.secs
 op_digits.secs <- options()$digits.secs
-  
+
 # set digits.secs = NULL (default, truncates to nearest second)
 options(digits.secs = NULL)
 
@@ -121,7 +131,6 @@ options(digits.secs = op_digits.secs)
 # or specify via format \%OSn e.g., when writing to disk or external database
 # see ?strptime
 format(vdat$DET$Time[2], format = "\%Y-\%m-\%d \%H:\%M:\%OS6")
-
 }
 
 }
diff --git a/man/receiver_line_det_sim.Rd b/man/receiver_line_det_sim.Rd
index 70a4811c..8df77c71 100644
--- a/man/receiver_line_det_sim.Rd
+++ b/man/receiver_line_det_sim.Rd
@@ -100,20 +100,20 @@ dp <- receiver_line_det_sim(rngFun = pdrf)
 dp
 
 # Again with only 10 virtual fish and optional plot to see simulated data
-dp <- receiver_line_det_sim(rngFun = pdrf, nsim = 10, showPlot = T) # w/ optional plot
+dp <- receiver_line_det_sim(rngFun = pdrf, nsim = 10, showPlot = TRUE) # w/ optional plot
 dp
 
 # Again but six receivers and allow fish to pass to left and right of line
 dp <- receiver_line_det_sim(
   rngFun = pdrf, recSpc = rep(1000, 5),
-  outerLim = c(1000, 1000), nsim = 10, showPlot = T
+  outerLim = c(1000, 1000), nsim = 10, showPlot = TRUE
 )
 dp
 
 # Again but four receivers with irregular spacing
 dp <- receiver_line_det_sim(
   rngFun = pdrf, recSpc = c(2000, 4000, 2000),
-  outerLim = c(1000, 1000), nsim = 10, showPlot = T
+  outerLim = c(1000, 1000), nsim = 10, showPlot = TRUE
 )
 dp
 
@@ -180,7 +180,7 @@ plot(edrf(0:2000),
 )
 
 # use empirical curve (edrf) in simulation
-dp <- receiver_line_det_sim(rngFun = edrf, nsim = 10, showPlot = T) # w/ optional plot
+dp <- receiver_line_det_sim(rngFun = edrf, nsim = 10, showPlot = TRUE) # w/ optional plot
 dp
 
 }
diff --git a/man/vrl2csv-deprecated.Rd b/man/vrl2csv-deprecated.Rd
index 28f5e77a..cf90cd60 100644
--- a/man/vrl2csv-deprecated.Rd
+++ b/man/vrl2csv-deprecated.Rd
@@ -64,7 +64,7 @@ vrl2csv(dirname(myVRL)) # directory input
 vrl2csv(myVRL) # file name input
 
 # setting 'overwrite=FALSE' will make new file with '_n'added to name
-vrl2csv(myVRL, overwrite = F)
+vrl2csv(myVRL, overwrite = FALSE)
 }
 
 }
diff --git a/man/write_vdat_csv.Rd b/man/write_vdat_csv.Rd
index fdf19361..c2b9080a 100644
--- a/man/write_vdat_csv.Rd
+++ b/man/write_vdat_csv.Rd
@@ -58,6 +58,7 @@ Write a vdat_list object to disk in Innovasea Fathom VDAT CSV format
 Writing is done via \code{\link[data.table]{fwrite}}.
 }
 \examples{
+\dontrun{
 
 # Example 1. Read and write a single file
 
@@ -80,7 +81,7 @@ write_vdat_csv(vdat)
 
 # write to multiple files
 write_vdat_csv(vdat, output_format = "csv.fathom.split")
-
+}
 }
 \author{
 C. Holbrook (cholbrook@usgs.gov)