documented available BRNG generators

Author: oleksandr-pavlyk

README.md

@@ -3,7 +3,7 @@
 
 `mkl_random` has started as Intel (R) Distribution for Python optimizations for NumPy.
 
-Per NumPy's community suggestions, voiced in https://github.com/numpy/numpy/pull/8209, it is being released as a 
+Per NumPy's community suggestions, voiced in https://github.com/numpy/numpy/pull/8209, it is being released as a
 stand-alone package.
 
 Prebuilt `mkl_random` can be installed into conda environment from Intel's channel on Anaconda cloud:
@@ -20,13 +20,27 @@ For distributions directly supported in Intel (R) Math Kernel Library (MKL), `me
    mkl_random.standard_normal(size=(10**5, 10**3), method='BoxMuller')
 ```
 
-Additionally, `mkl_random` exposes different basic random number generation algorithms available in MKL. For example to use `SFMT19937` use 
+Additionally, `mkl_random` exposes different basic random number generation algorithms available in MKL. For example to use `SFMT19937` use
 
 ```
    mkl_random.RandomState(77777, brng='SFMT19937')
 ```
 
 For generator families, such that `MT2203` and Wichmann-Hill, a particular member of the family can be chosen by specifying ``brng=('WH', 3)``, etc.
 
-See MKL reference guide for more details: 
-   https://software.intel.com/en-us/mkl-developer-reference-c-random-number-generators
+See MKL reference guide for more details:
+   https://software.intel.com/en-us/mkl-developer-reference-c-random-number-generators
+
+The list of supported by `mkl_random.RandomState` constructor `brng` keywords is as follows:
+
+  * 'MT19937'
+  * 'SFMT19937'
+  * 'WH' or ('WH', id)
+  * 'MT2203' or ('MT2203', id)
+  * 'MCG31'
+  * 'R250'
+  * 'MRG32K3A'
+  * 'MCG59'
+  * 'PHILOX4X32X10'
+  * 'NONDETERM'
+  * 'ARS5'