DOC - update documentation (#242)

Author: Badr-MOUFAD

CITATION.bib

@@ -0,0 +1,6 @@
+@inproceedings{skglm,
+    title     = {Beyond L1: Faster and better sparse models with skglm},
+    author    = {Q. Bertrand and Q. Klopfenstein and P.-A. Bannier and G. Gidel and M. Massias},
+    booktitle = {NeurIPS},
+    year      = {2022},
+}

doc/conf.py

@@ -15,6 +15,7 @@
 import os
 import sys
 import warnings
+from datetime import date
 
 import sphinx_gallery  # noqa
 import sphinx_bootstrap_theme  # noqa
@@ -95,7 +96,7 @@
 
 # General information about the project.
 project = u'skglm'
-copyright = u'2022, skglm developers'
+copyright = f'2022-{date.today().year}, skglm developers'
 author = u'skglm developers'
 
 # The version info for the project you're documenting, acts as replacement for

doc/index.rst

@@ -6,12 +6,12 @@
 =========
 ``skglm``
 =========
-*— A fast and modular scikit-learn replacement for sparse GLMs —*
+*— A fast and modular scikit-learn replacement for regularized GLMs —*
 
 --------
 
 
-``skglm`` is a Python package that offers **fast estimators** for sparse Generalized Linear Models (GLMs)
+``skglm`` is a Python package that offers **fast estimators** for regularized Generalized Linear Models (GLMs)
 that are **100% compatible with** ``scikit-learn``. It is **highly flexible** and supports a wide range of GLMs.
 You get to choose from ``skglm``'s already-made estimators or **customize your own** by combining the available datafits and penalties.
 
@@ -21,7 +21,7 @@ Get a hands-on glimpse on ``skglm`` through the :ref:`Getting started page <gett
 Why ``skglm``?
 --------------
 
-``skglm`` is specifically conceived to solve sparse GLMs.
+``skglm`` is specifically conceived to solve regularized GLMs.
 It supports many missing models in ``scikit-learn`` and ensures high performance.
 
 There are several reasons to opt for ``skglm`` among which:
@@ -57,10 +57,6 @@ It is also available on conda-forge and can be installed using, for instance:
 With ``skglm`` being installed, Get the first steps with the package via the :ref:`Getting started section <getting_started>`.
 Other advanced topics and uses-cases are covered in :ref:`Tutorials <tutorials>`.
 
-.. note::
-
-  - Currently, ``skglm`` is unavailable on Conda but will be released very soon...
-
 
 Cite
 ----
@@ -77,6 +73,7 @@ You are free to use it and if you do so, please cite
                      and G. Gidel and M. Massias},
         booktitle = {NeurIPS},
         year      = {2022},
+    }
 
 
 .. it is mandatory to keep the toctree here although it doesn't show up in the page
@@ -93,4 +90,3 @@ You are free to use it and if you do so, please cite
     api.rst
     contribute.rst
     changes/whats_new.rst
-