fix typos

R/self-similarity.R

@@ -45,7 +45,7 @@ NULL
 #'
 #' @param img An image in form of a matrix or array of numeric values,
 #'   preferably by square size. If the input is not square, bilinear resizing to
-#'   a square size is performade using the
+#'   a square size is performed using the
 #'   \code{\link[OpenImageR:resizeImage]{OpenImageR}} package. Use e.g.
 #'   \code{\link{img_read}()} to read an image file into \code{R}.
 #' @param full logical. Should the full frequency range be used for

R/symmetry.R

@@ -7,7 +7,7 @@ NULL
 #' image. Higher values indicate higher image symmetry.
 #'
 #' @details The function returns the vertical and horizontal mirror symmetry of
-#'   an image \code{img}. Symmetry values can range between 0 (not symmmetrical)
+#'   an image \code{img}. Symmetry values can range between 0 (not symmetrical)
 #'   and 1 (fully symmetrical). If \code{vertical} or \code{horizontal} is set
 #'   to \code{FALSE} then vertical or horizontal symmetry is not computed,
 #'   respectively.
@@ -31,7 +31,7 @@ NULL
 #'   maximal score is computed as the image's overall symmetry. Advanced users
 #'   can reverse this order by setting \code{per_channel = FALSE}. This results
 #'   in first computing the weighted averages for each position of the mirror
-#'   axis seperately, and afterwards finding the maximal overall symmetry score.
+#'   axis separately, and afterwards finding the maximal overall symmetry score.
 #'
 #'
 #' @param img An image in form of a matrix or array of numeric values. Use e.g.

README.md

@@ -78,7 +78,7 @@ img_symmetry(rails, horizontal = FALSE)
 
 See the [getting started vignette](https://stm.github.io/imagefluency/articles/getting-started.html) for a detailed introduction (or type `vignette('getting-started', package = 'imagefluency')` into the R console) and the [reference page](https://stm.github.io/imagefluency/reference/index.html) for details on each function. 
 
-If you are analyzing a larger number of images, make sure to read the tutorial on how to [analyze multiple imeages at once](https://stm.github.io/imagefluency/articles/batch-processing.html).
+If you are analyzing a larger number of images, make sure to read the tutorial on how to [analyze multiple images at once](https://stm.github.io/imagefluency/articles/batch-processing.html).
 
 ## Citation
 

vignettes/batch-processing.Rmd

@@ -135,7 +135,7 @@ If you plan to include typicality in your image analysis next to other metrics,
 
 ## Reading all images into memory using lapply()
 
-In the example below, I'll use base R's `lapply()` function to read all images. However, you can of course use purrr's `map()` function just as well.
+In the example below, I'll use base R's `lapply()` function to read all images. However, you can of course use `purrr::map()` just as well.
 
 Using `lapply()` is very simple: We tell R to apply the `img_read()` function to every element in the `fileNames` object. The result is a list where every list element contains an image matrix. Thus, this object might be very big!