small adjustments to the types section

docs/types.md

@@ -8,7 +8,7 @@ mobest employs a number of basic [S3 data types](http://adv-r.had.co.nz/S3.html)
 
 ### Spatial coordinates
 
-`mobest::create_geopos()` creates an object of class `mobest_spatialpositions` which is a `tibble` that represents spatial positions. Spatial positions in mobest are always 2-dimensional coordinates in a Cartesian space. For real world coordinates that means, that they have to be transformed to a projected coordinate system (e.g. with `sf::st_transform`): mobest can not be used with longitude and latitude coordinates (also see See {ref}`Projecting the spatial data <basic:projecting the spatial data>`).
+`mobest::create_geopos()` creates an object of class `mobest_spatialpositions` which is a `tibble` that represents spatial positions. Spatial positions in mobest are always 2-dimensional coordinates in a Cartesian space. For real world coordinates that means, that they have to be transformed to a projected coordinate system (e.g. with `sf::st_transform()`): mobest can not be used with longitude and latitude coordinates (see {ref}`Projecting the spatial data <basic:projecting the spatial data>`).
 
 ```r
 mobest::create_geopos(
@@ -18,11 +18,11 @@ mobest::create_geopos(
 )
 ```
 
-For the interpolation fields we often want regular, spatial grids covering a specific spatial area. These can be constructed with `mobest::create_prediction_grid`, which takes an object of class `sf` with polygons in a projected coordinate system. It also yields an object of class `mobest_spatialpositions`.
+For the interpolation fields we often want regular, spatial grids covering a specific spatial area. These can be constructed with `mobest::create_prediction_grid()`, which takes an object of class `sf` with polygons in a projected coordinate system. It also yields an object of class `mobest_spatialpositions`.
 
 ### Spatiotemporal coordinates
 
-`mobest_spatialpositions` can be transformed to `mobest_spatiotemporalpositions` with `mobest::geopos_to_spatpos`. This function calculates the permutations of all spatial positions with a new, implicitly temporal dimension `z`. `mobest_spatiotemporalpositions` is also derived from `tibble`.
+`mobest_spatialpositions` can be transformed to `mobest_spatiotemporalpositions` with `mobest::geopos_to_spatpos()`. This function calculates the permutations of all spatial positions with a new, implicitly temporal dimension `z`. `mobest_spatiotemporalpositions` is also derived from `tibble`.
 
 ```r
 mobest::geopos_to_spatpos(
@@ -35,7 +35,7 @@ mobest::geopos_to_spatpos(
 )
 ```
 
-`mobest::create_spatpos` directly creates `mobest_spatiotemporalpositions` objects to represent spatiotemporal positions.
+`mobest::create_spatpos()` directly creates `mobest_spatiotemporalpositions` objects to represent spatiotemporal positions.
 
 ```r
 mobest::create_spatpos(
@@ -57,11 +57,11 @@ mobest::create_obs(
 )
 ```
 
-Names and number of the components can be choosen freely, so instead of `ac1` + `ac2` as in the example here, one could, for example, also have `PC1` + `PC2` + `PC3`, or `MDS1` + `MDS2`.
+Names and number of the components can be chosen freely, so instead of `ac1` + `ac2` as in the example here, one could, for example, also have `PC1` + `PC2` + `PC3`, or `MDS1` + `MDS2`.
 
 ### Kernel parameter settings
 
-Gaussian process regression requires a parametrized covariance function: a "kernel". One `mobest_kernel` can be constructed with `mobest::create_kernel`.
+Gaussian process regression requires a parametrized covariance function: a "kernel". One `mobest_kernel` can be constructed with `mobest::create_kernel()`.
 
 ```r
 mobest::create_kernel(
@@ -77,7 +77,7 @@ mobest::create_kernel(
 )
 ```
 
-`mobest_kernel` includes these input paramaters in the form of an R `list`. It only represents one specific kernel, though, for one specific dependent variable (e.g. an ancestry component `ac1`). To account for the fact that a mobest analysis typically involves multiple genetic dimensions `mobest::create_kernset` provides a wrapper to bundle multiple named (by the dependent variable name) kernels directly in an object of class `mobest_kernelsetting`.
+`mobest_kernel` includes these input parameters in the form of an R `list`. It only represents one specific kernel, though, for one specific dependent variable (e.g. an ancestry component `ac1`). To account for the fact that a mobest analysis typically involves multiple genetic dimensions `mobest::create_kernset()` provides a wrapper to bundle multiple named (by the dependent variable name) kernels directly in an object of class `mobest_kernelsetting`.
 
 ```r
 mobest::create_kernset(
@@ -94,10 +94,10 @@ When working with real data we often need to explore permutations of data or acc
 
 Available are:
 
-- `mobest_spatialpositions_multi` (`mobest::create_geopos_multi`)
-- `mobest_spatiotemporalpositions_multi` (`mobest::create_spatpos_multi`)
-- `mobest_observations_multi` (`mobest::create_obs_multi`)
-- `mobest_kernelsetting_multi` (`mobest::create_kernset_multi`)
+- `mobest_spatialpositions_multi` (`mobest::create_geopos_multi()`)
+- `mobest_spatiotemporalpositions_multi` (`mobest::create_spatpos_multi()`)
+- `mobest_observations_multi` (`mobest::create_obs_multi()`)
+- `mobest_kernelsetting_multi` (`mobest::create_kernset_multi()`)
 
 And here is an example how they can be filled with named arguments: