Update README.rst

README.rst

@@ -7,8 +7,6 @@ cLHS: Conditioned Latin Hypercube Sampling
    :target: https://clhs-py.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status
 
-.. include-marker-do-not-remove
-
 .. |GitHub| image:: https://img.shields.io/badge/GitHub-clhs__py-informational.svg
    :target: https://github.com/wagoner47/clhs_py
 
@@ -18,17 +16,17 @@ cLHS: Conditioned Latin Hypercube Sampling
 .. |license| image:: https://img.shields.io/badge/License-MIT-blue.svg
    :target: https://github.com/wagoner47/clhs_py/blob/master/LICENSE.rst
 
-Conditioned Latin Hypercube Sampling in Python.
-
-This code is based on the conditioned LHS method of
-`Minasny & McBratney (2006)`_. It follows some of the code from the R package
+This **Python** package is based on the **Conditioned Latin Hypercube Sampling (cLHS)**
+method of `Minasny & McBratney (2006)`_. It follows some of the code from the **R** package
 clhs_ of Roudier et al.
 
-In short, this code attempts to create a Latin Hypercube sample by selecting
-only from input data. It uses simulated annealing to force the sampling to
-converge more rapidly, and also allows for setting a stopping criterion on
-the objective function described in Minasny & McBratney (2006).
+- It attempts to create a Latin Hypercube sample by selecting only from input data. 
+- It uses simulated annealing to force the sampling to converge more rapidly.
+- It allows for setting a stopping criterion on the objective function described in `Minasny & McBratney (2006)`_.
+
+You may reproduce the jupyter notebook example on `Binder <https://mybinder.org/v2/gh/wagoner47/clhs_py.git/master?filepath=%2Fdocs%2Fnotebooks>`_.
 
+Please check `online documentation <https://clhs-py.readthedocs.io/en/latest/>`_ for more information.
 
 
 .. _Minasny & McBratney (2006): https://doi.org/10.1016/j.cageo.2005.12.009