Merge pull request #117 from jbytecode/paper

update bibtex and manuscript

paper/paper.bib

@@ -192,7 +192,7 @@ @article{Richie-Halford:2021
     number = {58},
     pages = {3024},
     author = {Adam Richie-Halford and Manjari Narayan and Noah Simon and Jason Yeatman and Ariel Rokem},
-    title = {Groupyr: Sparse Group Lasso in Python},
+    title = {Groupyr: Sparse Group Lasso in {P}ython},
     journal = {Journal of Open Source Software}
 }
 
@@ -221,7 +221,7 @@ @article{Bertrand:2022
 
 @article{Zhu:2022,
     author  = {Jin Zhu and Xueqin Wang and Liyuan Hu and Junhao Huang and Kangkang Jiang and Yanhang Zhang and Shiyun Lin and Junxian Zhu},
-    title   = {abess: A Fast Best-Subset Selection Library in Python and R},
+    title   = {abess: A Fast Best-Subset Selection Library in {P}ython and {R}},
     journal = {Journal of Machine Learning Research},
     year    = {2022},
     volume  = {23},
@@ -314,7 +314,7 @@ @article{Xie:2023
 }
 
 @article{Zhong:2022,
-  title = {An \$\{\textbackslash ensuremath\{\textbackslash ell\}\}\_\{0\}\{\textbackslash ensuremath\{\textbackslash ell\}\}\_\{2\}\$-Norm Regularized Regression Model for Construction of Robust Cluster Expansions in Multicomponent Systems},
+  title = {An L0 L2-Norm Regularized Regression Model for Construction of Robust Cluster Expansions in Multicomponent Systems},
   author = {Zhong, Peichen and Chen, Tina and Barroso-Luque, Luis and Xie, Fengyu and Ceder, Gerbrand},
   year = {2022},
   journaltitle = {Physical Review B},

paper/paper.md

@@ -75,7 +75,7 @@ linear regression algorithms within a single package.
 
 Statistical regression models with structured sparsity (involving grouped covariates,
 sparse grouped covariates, and hierarchical relationships between covariates terms)
-parametrized via Group Lasso or Best Subset Selection based objetives have been used in a
+parametrized via Group Lasso or Best Subset Selection based objectives have been used in a
 wide range of scientific disciplines, including genomics [@Chen:2021], bioinformatics [@Ma:2007],
 medicine [@Kim:2012], econometrics [@Athey:2017], chemistry [@Gu:2018], and materials science
 [@Leong:2019]. The flexible implementation of sparse linear regression models in `sparse-lm`
@@ -161,7 +161,9 @@ options are implemented. The implemented models are listed below:
 ## Implemented regression models
 
 The table below shows the regression models that are implemented in `sparse-lm` as well
-as available implementations in other Python packages. $\checkmark$ indicates that the
+as available implementations in other Python packages. $\checkmark$ indicates that the model selected
+is applicable by the package located in the corresponding column.
+
 
 |             Model             |  `sparse-lm` |    `celer` |   `groupyr` | `group-lasso` |    `skglm`   |    `abess`   |
 |:-----------------------------:|:------------:|:---------:|:-----------:|:-----------:|:------------:|:------------:|

requirements.txt

@@ -2,4 +2,4 @@ numpy >=1.23
 cvxpy >=1.2
 scikit-learn > 1.2
 scipy >=1.9
-joblib
+joblib