Updated readme.

.gitignore

@@ -11,3 +11,4 @@ tests/playground
 .vimrc
 src/
 dist/
+__pycache__

README.md

-# ChainsAddiction
-
-ChainsAddiction is a tool for simple training discrete-time Hidden Markov
-Models. It is written in `C` and features a `numpy`-based Python extension
-module.
-
-## Installation
-Clone this repository, change to its root directory and issue
-
-    pip install .
-
-## Working with the C API
-
-## Working with the Python interpreter
-Calling Chains_addiction from `Python` is simple as pie. You just need to import
-it:
-
-    import chains_addiction as ca
-    ca.hmm_poisson_fit_em(x, m, init_means, init_tpm, int_sd, max_iter=1000, tol=1e-5)
-
-## Notes
-- Currently only Poisson-distributed HMM are implemented.
-- ChainsAddiction does not support Python 2. Specifically, it requires `Python >= 3.5` and `numpy >= 1.16`.
\ No newline at end of file

README.rst

+=======================================
+ChainsAddiction 
+=======================================
+
+ChainsAddiction is a tool for simple training discrete-time Hidden Markov
+Models. It is written in C and features a numpy-based Python extension
+module.
+
+Installation
+=======================================
+
+Clone this repository, change to its root directory and issue
+
+.. code-block:: bash
+
+    pip install .
+
+Working with chainsaddiction 
+=======================================
+Calling chainsaddiction from Python is simple as pie. You just need to import
+it:
+
+.. code-block:: python
+
+    import chainsaddiction as ca
+    ca.hmm_poisson_fit_em(trainig_data, m_states, init_means, init_tpm,
+                          int_sd, max_iter=1000, tol=1e-5)
+
+Notes
+---------------------------------------
+* Currently only algorithms for Poisson-distributed HMMs are implemented.
+* ChainsAddiction does not support Python 2. Specifically, it requires `Python >= 3.7` and `numpy >= 1.16`.

setup.cfg

 [metadata]
 name = chainsaddiction
-version = 0.1
+version = 0.1.1
 description = Discrete time, finit state space, stationary Hidden Markov Model.
 long_description = file: README.md
 license = BSD 3-Clause License