From: Benjamin Auder <benjamin.auder@somewhere>
Date: Fri, 22 Jul 2016 16:35:04 +0000 (+0200)
Subject: readme.md
X-Git-Url: https://git.auder.net/variants/Chakart/css/assets/doc/%24%7BgetWhatsApp%28link%29%7D?a=commitdiff_plain;h=dab5fbe952aa311f3d53bf3799263d46a2f44acd;p=synclust.git

readme.md
---

diff --git a/README.html b/README.html
deleted file mode 100755
index 8b45885..0000000
--- a/README.html
+++ /dev/null
@@ -1,20 +0,0 @@
-<h1>Clustering into regions of synchronous variations</h1>
-
-<p>Joint work with <a href="http://www.cmap.polytechnique.fr/~giraud/">Christophe Giraud</a>.</p>
-
-<hr/>
-
-<p>This R package implements a clustering method described in detail in <a href="http://www.cmap.polytechnique.fr/~giraud/SynchronousPop.pdf">this article</a>.</p>
-
-<p>The goal is to divide a map of observational sites into regions of similar intra-variability over the years, but 
-with distinct dynamics compared to other regions.
-The problem has both spatial (the sites) and temporal (measurements every year) aspects, and is difficult because 
-there are no a priori indications about the regions.</p>
-
-<hr/>
-
-<p>Two (heuristic) methods are available in the package, either direct graph clustering or parameters estimation 
-in a model-based formulation of the problem.</p>
-
-<p>
-Main function to cluster populations data: <code>findSyncVarRegions()</code>.</p>
diff --git a/README.md b/README.md
new file mode 100755
index 0000000..3cde5c8
--- /dev/null
+++ b/README.md
@@ -0,0 +1,19 @@
+# Clustering into regions of synchronous variations
+
+Joint work with  [Christophe Giraud](http://www.cmap.polytechnique.fr/~giraud/).
+
+---
+
+This R package implements a clustering method described in detail in [this article](http://www.cmap.polytechnique.fr/~giraud/SynchronousPop.pdf).
+
+The goal is to divide a map of observational sites into regions of similar intra-variability over the years, but 
+with distinct dynamics compared to other regions.
+The problem has both spatial (the sites) and temporal (measurements every year) aspects, and is difficult because 
+there are no a priori indications about the regions.
+
+---
+
+Two (heuristic) methods are available in the package, either direct graph clustering or parameters estimation 
+in a model-based formulation of the problem.
+
+Main function to cluster populations data: `findSyncVarRegions()`.