version bump and update of README

Author: nevrome

DESCRIPTION

@@ -3,7 +3,7 @@ Title: Spatiotemporal Ancestry Interpolation and Search
 Description: 
   Types and functions for spatiotemporal interpolation of human genetic ancestry components,
   similarity search and the calculation of a derived measure of mobility.
-Version: 0.7.0
+Version: 1.0.0
 Authors@R:
     c(
       person(given = "Clemens",

NEWS.md

@@ -1,3 +1,4 @@
+- v1.0.0: Version bump for the publication with some minor changes in the documentation
 - v0.7.0: Brought back a now more flexible option to calculate distance fractions in `summarize_origin_vectors()`
 - v0.6.0: Implemented normalization (as a default) for `locate(_multi)()`, `multiply_dependent_probabilities()` and `fold_probabilities_per_group()`
 - v0.5.0: Added extracting/subsetting with `[` for the input list data types

README.Rmd

@@ -18,7 +18,7 @@ This R package provides types and functions for spatiotemporal interpolation of
 
 0. `mobest` assumes you have a set of genetic samples with spatial (two coordinates in a projected reference system) and temporal positions (years BC/AD) for which you calculated a derived, numeric measure of genetic ancestry (e.g. coordinates in a PCA or MDS space).
 1. `mobest` provides a framework to perform spatiotemporal interpolation using Gaussian process regression (kriging) with the [`laGP`](https://CRAN.R-project.org/package=laGP) package to reconstruct an ancestry field based on the ancestry measure you provided.
-2. `mobest` allows to derive a similarity probability for samples of interest within the interpolated field, which -- under certain circumstances -- can be interpreted as an origin probability. 
+2. `mobest` allows to derive a similarity probability for samples of interest within the interpolated field, which -- under certain circumstances -- can be interpreted as an origin probability. See the [example gif](man/figures/) on the right.
 3. `mobest` finally introduces functions to estimate and summarize a measure of mobility for the samples of interest, based on the similarity probability field.
 
 Here is a simple, artificial example how 2. can be used: