minor fixes in the documentation

docs/basic.md

@@ -53,7 +53,7 @@ research_area_4326 <- sf::st_polygon(
 ) %>% sf::st_sfc(crs = 4326)
 ```
 
-Spatial coordinates require a coordinate references system (CRS). For lat-lon coordinates we typically use [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System#WGS84) with the [EPSG code](https://en.wikipedia.org/wiki/EPSG_Geodetic_Parameter_Dataset) 4326. `st_polygon()` creates a simple polygon as a clockwise arrangement of individual coordinates and `st_sfc()` properly defines this polygon as a geographic area on Earth. A simple way to interactively inspect this polygon on a world map in R is provided by the mapview package: `mapview::mapview(research_area_4326)`.
+Spatial coordinates require a coordinate references system (CRS). For lat-lon coordinates we typically use [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System#WGS84) with the [EPSG code](https://en.wikipedia.org/wiki/EPSG_Geodetic_Parameter_Dataset) 4326. `st_polygon()` creates a simple polygon as a clockwise arrangement of individual coordinates and `st_sfc()` properly defines this polygon as a geographic area on Earth. A simple way to interactively inspect this polygon on a world map in R is provided by the `mapview` package: `mapview::mapview(research_area_4326)`.
 
 ```{figure} img/basic/mapview_research_area.png
 The defined research area plotted on top of a map.
@@ -193,7 +193,7 @@ readr::write_csv(samples_basic, file = "docs/data/samples_basic.csv")
 
 You do not have to run this and can instead download the example dataset table `samples_basic.csv` from [here](data/samples_basic.csv). 
 
-When you have download the input data file you can load it into a `tibble` in R.
+When you have downloaded the input data file you can load it into a `tibble` in R.
 
 ```r
 samples_basic <- readr::read_csv("docs/data/samples_basic.csv")

docs/install.md

@@ -11,7 +11,7 @@ remotes::install_github('nevrome/mobest')
 
 You can also install specific/older versions of mobest with the following syntax: `nevrome/mobest[@ref|#pull|@*release]`. For example to install the publication release version you can run `remotes::install_github('nevrome/[email protected]')`.
 
-For any of this too work a number of **system libraries** (mostly for processing geospatial data) have to be installed on your system, primarily for one particular dependency of mobest: the **`sf` R package**. The following table includes the libraries and the names of the relevant packages in the package management systems of various Linux distributions and MacOS.
+For any of this to work a number of **system libraries** (mostly for processing geospatial data) have to be installed on your system, primarily for one particular dependency of mobest: the **`sf` R package**. The following table includes the libraries and the names of the relevant packages in the package management systems of various Linux distributions and MacOS.
 
 | System library                                                        | deb package<br>(Ubuntu/Debian) | rpm package<br>(Fedora/CentOS) | pkgbuild package<br>(Arch) | brew package<br>(MacOS) |
 |-----------------------------------------------------------------------|----------------------------------|---------------------------------|-------------------------|----------------------|

docs/welcome.md

@@ -2,7 +2,7 @@
 version](https://img.shields.io/github/r-package/v/nevrome/mobest) (latest version)
 [![R-CMD-check](https://github.com/nevrome/mobest/actions/workflows/check-release.yaml/badge.svg)](https://github.com/nevrome/mobest/actions/workflows/check-release.yaml)
 
-This R package provides types and functions for spatiotemporal interpolation of human genetic ancestry components, probabilistic similarity search and the calculation of a derived measure for **mob**ility **est**imation. The workflow in mobest version 1.0.0 was specifically developed to support this research compendium: <https://github.com/nevrome/mobest.analysis.2022>, which in turn underpins {cite:p}`Schmid2023`.
+This R package provides types and functions for spatiotemporal interpolation of human genetic ancestry components, probabilistic similarity search and the calculation of a derived measure of ancestry relocation and mobility. The workflow in mobest version 1.0.0 was specifically developed to support this research compendium: <https://github.com/nevrome/mobest.analysis.2022>, which in turn underpins {cite:p}`Schmid2023`.
 
 ```{figure} img/example_movie.gif
 ---