fix typos

R/bbox.R

@@ -4,7 +4,7 @@
 #' @param buffer `[numeric(0)=NULL]{NULL, >0}` \cr An optional buffer of the bounding box.
 #' @template dots_none
 #' @name bbox
-#' @return If `spatial = FALSE` a named vector of length four with the extent of the bounding box. Otherwise a `SpatialPolygon` or a simple freature polygon with the bounding box.
+#' @return If `spatial = FALSE` a named vector of length four with the extent of the bounding box. Otherwise a `SpatialPolygon` or a simple feature polygon with the bounding box.
 #' @export
 #' @examples
 #' data(deer)

R/cleaning.R

@@ -227,7 +227,7 @@ check_time_unit <- function(tu) {
 #' Should be `"secs"`, `"mins"`, or `"hours"`. Ignored if `class(gamma) == "Period".`
 #' @param DOP `[character]` A character string giving the name of the column
 #' containing the dilution of precision (DOP) data. See details below.
-#' @param ... Addtional arguments. None currently implemented.
+#' @param ... Additional arguments. None currently implemented.
 #'
 #' @details Locations are considered duplicates if their timestamps are within
 #' `gamma` of each other. However, the function runs sequentially through the

R/distributions.R

@@ -155,12 +155,12 @@ make_gamma_distr <- function(shape = 1, scale = 1, vcov = NULL) {
 
 #' Sample random numbers
 #'
-#' Sample radom numbers from a distribution created within the `amt` package.
+#' Sample random numbers from a distribution created within the `amt` package.
 
 #' @param x `[amt_distr]` \cr A distribution object.
 #' @param n `[integer(1)=100]{>0}` \cr The number of random draws.
 #' @param ... none implemented.
-#' @return A numermic vector.
+#' @return A numeric vector.
 #' @export
 random_numbers <- function(x, n = 100, ...) {
   UseMethod("random_numbers")

R/hr_kde.R

@@ -142,7 +142,7 @@ hr_kde_ref_scaled <- function(
 
 #' `hr_kde_pi` wraps `KernSmooth::dpik` to select bandwidth for kernel density estimation the plug-in-the-equation method in two dimensions.
 #'
-#' This function calculates bandwidths for kernel density estimation by wrapping `KernSmooth::dpik`. If `correct = TURE`, the bandwidth is trasformed with power 5/6 to correct for using an univariate implementation for bivariate data (Gitzen et. al 2006).
+#' This function calculates bandwidths for kernel density estimation by wrapping `KernSmooth::dpik`. If `correct = TURE`, the bandwidth is transformed with power 5/6 to correct for using an univariate implementation for bivariate data (Gitzen et. al 2006).
 
 #' @template track_xy_star
 #' @template dots_none

R/track_align.R

@@ -3,7 +3,7 @@
  #' Functions to only selects relocations that can be aligned with a new time series (within some tolerance).
  #' @param x A track.
  #' @param new.times The new time trajectory.
- #' @param tolerance The tolerance between observed time stamps and new time stamps in seceonds. This should be either a vector of length 1 or length `new.times`.
+ #' @param tolerance The tolerance between observed time stamps and new time stamps in seconds. This should be either a vector of length 1 or length `new.times`.
  #' @template dots_none
  #' @return A `track_xyt`.
  #' @name track_align

vignettes/p6_uhc_plots.Rmd

@@ -145,7 +145,7 @@ hsf_right <- glm(case_ ~ forage + temp + I(temp^2) + pred + cover,
 
 #### Prepare UHC plot data
 
-Next, onto step (4), where we use `prep_uhc()` to prepare the UHC data. This is the function that does most of the heavy lifting (*i.e.*, the boostrapping).
+Next, onto step (4), where we use `prep_uhc()` to prepare the UHC data. This is the function that does most of the heavy lifting (*i.e.*, the bootstrapping).
 
 ```{r hsf prep_uhc}
 # Prep under wrong model
@@ -180,7 +180,7 @@ We can see these issues have been resolved for our `cover` variables, indicating
 Note that with a large number of bootstrap samples, the plots are quite complex and can take multiple seconds to render. If you are working with many candidate variables, this makes examining all the plot quite slow. We have included a few potential solutions to this problem:
 
   - First, you can plot just a single variable, by subsetting the `uhc_data` object by either integers or the names of the variables.
-  - Second, you can convert the many lines to a single polygon representing a confidence enevelope (with a confidence level of your choice).
+  - Second, you can convert the many lines to a single polygon representing a confidence envelope (with a confidence level of your choice).
 
 Subsetting: