Merge pull request #37 from afraniomelo/main

adjustment in readme

README.md

@@ -16,10 +16,14 @@ For further information, please refer to the [documentation](https://bibmon.read
 Installation
 ----------------------
 
-`BibMon` can be installed using `pip`:
+`BibMon` can be installed using [pip](https://pypi.org/project/bibmon/):
 
     pip install bibmon
 
+Or [conda](https://anaconda.org/conda-forge/bibmon):
+
+    conda install conda-forge::bibmon
+
 Available Models
 ----------------------
 
@@ -39,7 +43,8 @@ Essentially, the library is used in two steps:
 
 Specifically, the implemented control charts are based on squared prediction error (SPE).
 
-The examples in the `notebooks/` directory demonstrate the main functionalities of `BibMon`. The API reference can be generated using the Sphynx package from the files in the `docs/` directory.
+For more details, please refer to the [tutorials](https://bibmon.readthedocs.io/en/latest/tutorials.html) available in the documentation to learn about the main functionalities of `BibMon`. You can find the corresponding Jupyter Notebooks for these tutorials in the `docs/source/` directory.
+
 
 Features
 ----------------------

docs/source/install.rst

@@ -6,10 +6,17 @@ This section covers the installation of BibMon.
 Installing from PyPI
 --------------------------------
 
-To install Requests, you can use pip::
+To install BibMon, you can use pip::
 
     $ pip install bibmon
 
+Installing from conda-forge
+--------------------------------
+
+Alternatively, you can use conda::
+
+    $ conda install conda-forge::bibmon
+
 Get the Source Code
 -------------------
 

setup.py

@@ -29,7 +29,7 @@
     name='bibmon',
     description='Library with routines for data-driven process monitoring.',
     license='Apache 2.0',
-    version='1.1.3',
+    version='1.1.4',
     long_description=README,
     long_description_content_type='text/markdown',
     url='https://github.com/petrobras/bibmon',