Finalised 0.1.7 & master branch as the dev branch

Author: PyDataBlog

README.md

@@ -49,7 +49,7 @@ pkg> add ParallelKMeans
 For the few (and selected) brave ones, one can simply grab the current experimental features by simply adding the experimental branch to your development environment after invoking the package manager with `]`:
 
 ```julia
-pkg> add ParallelKMeans#experimental
+pkg> add ParallelKMeans#master
 ```
 
 To revert to a stable version, you can simply run:

docs/src/index.md

@@ -48,10 +48,10 @@ You can grab the latest stable version of this package from Julia registries by
 pkg> add ParallelKMeans
 ```
 
-The few (and selected) brave ones can simply grab the current experimental features by simply adding the experimental branch to your development environment after invoking the package manager with `]`:
+The few (and selected) brave ones can simply grab the current experimental features by simply adding the master branch to your development environment after invoking the package manager with `]`:
 
 ```julia
-pkg> add ParallelKMeans#experimental
+pkg> add ParallelKMeans#master
 ```
 
 You are good to go with bleeding edge features and breakages!
@@ -174,6 +174,7 @@ c = [ParallelKMeans.kmeans(X, i; tol=1e-6, max_iters=300, verbose=false).totalco
 Currently, this package is benchmarked against similar implementations in both Python and Julia. All reproducible benchmarks can be found in [ParallelKMeans/extras](https://github.com/PyDataBlog/ParallelKMeans.jl/tree/master/extras) directory. More tests in various languages are planned beyond the initial release version (`0.1.0`).
 
 *Note*: All benchmark tests are made on the same computer to help eliminate any bias.
+
 |PC Name                      |CPU                       |Ram               |
 |:---------------------------:|:------------------------:|:----------------:|
 |iMac (Retina 5K 27-inch 2019)|3 GHz 6-Core Intel Core i5|8 GB 2667 MHz DDR4|