Updated documentation

Author: Jfortin1

README.md

@@ -15,3 +15,61 @@ neuroCombat is hosted on PyPI, and the easiest way to install neuroCombat is to
 ```
 pip install neuroCombat
 ```
+
+## Usage
+
+The ```neuroCombat``` function performs harmonization 
+
+```python
+from neuroCombat import neuroCombat
+import pandas as pd
+import numpy as np
+
+# Getting example data
+# 200 rows (features) and 10 columns (scans)
+data = np.genfromtxt('testdata/testdata.csv', delimiter=",", skip_header=1)
+
+# Specifying the batch (scanner variable) as well as a biological covariate to preserve:
+covars = {'batch':[1,1,1,1,1,2,2,2,2,2],
+          'gender':[1,2,1,2,1,2,1,2,1,2]} 
+covars = pd.DataFrame(covars)  
+
+# To specify names of the variables that are categorical:
+categorical_cols = ['gender']
+
+# To specify the name of the variable that encodes for the scanner/batch covariate:
+batch_col = 'batch'
+
+#Harmonization step:
+data_combat = neuroCombat(dat=data,
+    covars=covars,
+    batch_col=batch_col,
+    categorical_cols=categorical_cols)["data"]
+```
+
+## Optional arguments
+
+- `eb` : `True` or `False`. Should Empirical Bayes be performed? If `False`, the harmonization model will be fit for each feature separately. This is equivalent to performing a location/shift (L/S) correction to each feature separately (no information pooling across features). 
+
+- `parametric` : `True` or `False`. Should parametric adjustements be performed? `True` by default. 
+
+- `mean_only` : `True` or `False`. Should only be means adjusted (no scaling)? `False` by default
+
+- `ref_batch` : batch name to be used as the reference batch for harmonization. `None` by default, in which case the average across scans/images/sites is taken as the reference batch.
+
+## Output
+
+Since version 0.2.10, the `neuroCombat` function outputs a dictionary with 3 elements:
+- `data`: A numpy array of the harmonized data, with the same dimension (shape) as the input data.
+- `estimates`: A dictionary of the neuroCombat estimates; useful for visualization and understand scanner effects.
+- `info`: A dictionary of the inputs needed for ComBat harmonization (batch/scanner information, etc.)
+
+To simply return the harmonized data, one can use the following:
+
+```
+data_combat = neuroCombat(dat=dat, ...)["data"]
+```
+
+where `...` are the user-specified arguments needed for harmonization. 
+    
+