mention skglm

README.md

@@ -21,12 +21,40 @@ Currently, the package handles the following problems:
 | Multitask Lasso               | ✕               | ✓
 | Sparse Logistic regression    | ✕               | ✕
 
+If you are interested in other models, such as non convex penalties (SCAD, MCP), sparse group lasso, group logistic regression, Poisson regression, Tweedie regression, have a look at our companion package [``skglm``](https://github.com/scikit-learn-contrib/skglm)
 
+## Cite
+
+``celer`` is licensed under the [BSD 3-Clause](https://github.com/mathurinm/celer/blob/main/LICENSE). Hence, you are free to use it.
+If you do so, please cite:
+
+
+```bibtex
+@InProceedings{pmlr-v80-massias18a,
+  title     = {Celer: a Fast Solver for the Lasso with Dual Extrapolation},
+  author    = {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},
+  booktitle = {Proceedings of the 35th International Conference on Machine Learning},
+  pages     = {3321--3330},
+  year      = {2018},
+  volume    = {80},
+}
+
+@article{massias2020dual,
+  author  = {Mathurin Massias and Samuel Vaiter and Alexandre Gramfort and Joseph Salmon},
+  title   = {Dual Extrapolation for Sparse GLMs},
+  journal = {Journal of Machine Learning Research},
+  year    = {2020},
+  volume  = {21},
+  number  = {234},
+  pages   = {1-33},
+  url     = {http://jmlr.org/papers/v21/19-587.html}
+}
+```
 
 ## Why ``celer``?
 
 ``celer`` is specially designed to handle Lasso-like problems which makes it a fast solver of such problems.
-In particular it comes with tools such as:
+In particular, it comes with tools such as:
 
 - automated parallel cross-validation
 - support of sparse and dense data
@@ -39,7 +67,7 @@ In particular it comes with tools such as:
 
 ## Get started
 
-To get stared, install ``celer`` via pip
+To get started, install ``celer`` via pip
 
 ```shell
 pip install -U celer
@@ -56,21 +84,21 @@ run the following commands to fit a Lasso estimator on a toy dataset.
 >>> estimator.fit(X, y)
 ```
 
-This is just a starter examples.
+This is just a starter example.
 Make sure to browse [``celer`` documentation ](https://mathurinm.github.io/celer/) to learn more about its features.
 To get familiar with [``celer`` API](https://mathurinm.github.io/celer/api.html), you can also explore the gallery of examples
-which includes examples on real-life datasets as well as timing comparison with other solvers.
+which includes examples on real-life datasets as well as timing comparisons with other solvers.
 
 
 
 ## Contribute to celer
 
-``celer`` is an open source project and hence rely on community efforts to evolve.
+``celer`` is an open-source project and hence relies on community efforts to evolve.
 Your contribution is highly valuable and can come in three forms
 
 - **bug report:** you may encounter a bug while using ``celer``. Don't hesitate to report it on the [issue section](https://github.com/mathurinm/celer/issues).
 - **feature request:** you may want to extend/add new features to ``celer``. You can use the [issue section](https://github.com/mathurinm/celer/issues) to make suggestions.
-- **pull request:** you may have fixed a bug, enhanced the documentation, ... you can submit a [pull request](https://github.com/mathurinm/celer/pulls) and we will reach out to you asap.
+- **pull request:** you may have fixed a bug, enhanced the documentation, ... you can submit a [pull request](https://github.com/mathurinm/celer/pulls) and we will respond asap.
 
 For the last mean of contribution, here are the steps to help you setup ``celer`` on your local machine:
 
@@ -87,7 +115,7 @@ cd celer
 pip install -e .
 ```
 
-3. To run the gallery examples and build the documentation, run the followings
+3. To run the gallery examples and build the documentation, run the following
 
 ```shell
 cd doc
@@ -96,36 +124,10 @@ make html
 ```
 
 
-## Cite
-
-``celer`` is licensed under the [BSD 3-Clause](https://github.com/mathurinm/celer/blob/main/LICENSE). Hence, you are free to use it.
-If you do so, please cite:
-
-
-```bibtex
-@InProceedings{pmlr-v80-massias18a,
-  title     = {Celer: a Fast Solver for the Lasso with Dual Extrapolation},
-  author    = {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},
-  booktitle = {Proceedings of the 35th International Conference on Machine Learning},
-  pages     = {3321--3330},
-  year      = {2018},
-  volume    = {80},
-}
-
-@article{massias2020dual,
-  author  = {Mathurin Massias and Samuel Vaiter and Alexandre Gramfort and Joseph Salmon},
-  title   = {Dual Extrapolation for Sparse GLMs},
-  journal = {Journal of Machine Learning Research},
-  year    = {2020},
-  volume  = {21},
-  number  = {234},
-  pages   = {1-33},
-  url     = {http://jmlr.org/papers/v21/19-587.html}
-}
-```
 
 ## Further links
 
 - https://mathurinm.github.io/celer/
 - https://arxiv.org/abs/1802.07481
-- https://arxiv.org/abs/1907.05830
+- https://arxiv.org/abs/1907.05830
+