Merge pull request #9 from AdrianCurtin/master

Update readme file to be more descriptive

README.md

@@ -1 +1,44 @@
 # nirs-toolbox
+The Github repository for the AnalyzIR Toolbox
+
+The NIRS toolbox is s a Matlab based analysis program for NIRS that focuses on statistical analysis for functional and resting state studies. Developed an maintained by Dr. Ted Huppert's lab at University of Pittsburgh.
+http://huppertlab.net/nirs-toolbox-2/
+
+And can be sited as: Santosa, H., Zhai, X., Fishburn, F., & Huppert, T. (2018). The NIRS brain AnalyzIR toolbox. Algorithms, 11(5), 73.
+
+
+
+In order to get started with nirs toolbox first install Matlab and use github to download the latest release of nirs-toolbox. Then add the directories to Matlab
+
+## Add directories to the Matlab Path
+    Note folders that start with “+”
+    (e.g. /+nirs) denote Matlab namespaces
+    and cannot be added directly to the
+    path. You must add the parent folder
+    containing this namespace.
+
+1) Add folder
+<root>/nirs-toolbox
+
+2) Add with Subfolders
+<root>/nirs-toolbox/external
+<root>/nirs-toolbox/demos
+
+## Tutorial
+A full tutorial on nirs-toolbox is available here:
+http://huppertlab.net/wp-content/uploads/2018/05/1.1_Intro_Toolbox.pdf
+
+## Demos
+The toolbox includes several demos. Click below for a full walk through of some of these demos.
+
+### fnirs_analysis.m
+http://huppertlab.net/auto-draft/
+ A demo that shows the basic structure of the toolbox and introduces the data, probe, and channelStats classes. This shows a basic first level statistical analysis. Data is downloaded from the web for the example.
+
+### code_testing_demo.m
+http://huppertlab.net/code_testing_demo-m/
+This demo shows how to do regression testing of the toolbox’s main data types. This also demonstrates the built in receiver-operator-characteristic (ROC) analysis tools.
+
+### compare_software_demo.m
+http://huppertlab.net/compare_software_demo-m/ (Not yet completed)
+This demo takes data through HOMER, AR-IRLS, and NIRS-SPM tools to compare the sensitivity and specificity of the GLM statistics.