diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 6af47c2..d1679fd 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
+  rev: v4.5.0
   hooks:
   - id: check-yaml
   - id: fix-encoding-pragma
@@ -38,7 +38,7 @@ repos:
     - --profile=black
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v3.13.0
+  rev: v3.15.0
   hooks:
     - id: pyupgrade
       args: [--py38-plus]
@@ -71,6 +71,6 @@ repos:
   - id: rst-inline-touching-normal
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: 'v1.5.1'  # Use the sha / tag you want to point at
+    rev: 'v1.6.0'  # Use the sha / tag you want to point at
     hooks:
     -   id: mypy
diff --git a/paper/paper.md b/paper/paper.md
index 84e7e98..3f6be27 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -73,6 +73,17 @@ available solvers. `sparse-lm` satisfies the need for a flexible and comprehensi
 library that  enables easy experimentation and comparisons of different sparse
 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
+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`
+allows researchers to easily experiment and choose the best regression model for their
+specific problem. `sparse-lm` has already been used to build linear models with
+structured  sparsity in a handful of material science studies
+[@Barroso-Luque:2022; @Zhong:2022; @Xie:2023, @Zhong:2023].
+
 # Background
 
 ![Schematic of a linear model with grouped covariates with hierarchical relations.
@@ -137,18 +148,6 @@ introduce hierarchical structure into the model. Finally, we have also included
 $\ell_2$ regularization term controlled by the hyperparameter $\lambda$, which is useful
 when dealing with poorly conditioned design matrices.
 
-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
-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`
-allows researchers to easily experiment and choose the best regression model for their
-specific problem. `sparse-lm` has already been used to build linear models with
-structured  sparsity in a handful of material science studies
-[@Barroso-Luque:2022; @Zhong:2022; @Xie:2023, @Zhong:2023].
-
-
 # Usage
 
 Since the linear regression models in `sparse-lm` are implemented to be compatible with
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..8a30e10
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+numpy >=1.23
+cvxpy >=1.2
+scikit-learn > 1.2
+scipy >=1.9
+joblib
\ No newline at end of file