Merge pull request #75 from jbytecode/paper

Paper

paper/paper.bib

@@ -53,7 +53,7 @@ @article{williams:2010
 }
 
 @article{scagliarini:2023,
-  title = {Gradients of O-information: Low-order descriptors of high-order dependencies},
+  title = {Gradients of {O}-information: Low-order descriptors of high-order dependencies},
   author = {Scagliarini, T. and Nuzzi, D. and Antonacci, Y. and Faes, L. and Rosas, F. E. and Marinazzo, D. and Stramaglia, S.},
   journal = {Phys. Rev. Res.},
   volume = {5},
@@ -155,7 +155,7 @@ @article{marinazzo:2022
 
 @article{wollstadt:2018,
   author = {Patricia Wollstadt and Joseph T. Lizier and Raul Vicente and Conor Finn and Mario Martinez-Zarzuela and Pedro Mediano and Leonardo Novelli and Michael Wibral},
-  title = {IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks},
+  title = {IDTxl: The Information Dynamics Toolkit xl: a {P}ython package for the efficient analysis of multivariate information dynamics in networks},
   doi = {10.21105/joss.01081},
   url = {https://doi.org/10.21105/joss.01081},
   year = {2019},
@@ -167,7 +167,7 @@ @article{wollstadt:2018
 }
 
 @article{james:2018,
-  title = {``dit``: a Python package for discrete information theory},
+  title = {``dit``: a {P}ython package for discrete information theory},
   author = {Ryan G. James and Christopher J. Ellison and James P. Crutchfield},
   doi = {10.21105/joss.00738},
   url = {https://doi.org/10.21105/joss.00738},
@@ -180,7 +180,7 @@ @article{james:2018
 }
 
 @article{candadai:2019,
-  title = {infotheory: A C++/Python package for multivariate information theoretic analysis},
+  title = {infotheory: A {C}++/{P}ython package for multivariate information theoretic analysis},
   author = {Madhavun Candadai and Eduardo J. Izquierdo},
   doi = {10.21105/joss.01609},
   url = {https://doi.org/10.21105/joss.01609},
@@ -193,7 +193,7 @@ @article{candadai:2019
   }
 
 @article{madukaife:2024,
-  title={Estimation of Shannon differential entropy: An extensive comparative review},
+  title={Estimation of {S}hannon differential entropy: An extensive comparative review},
   author={Madukaife, Mbanefo S and Phuc, Ho Dang},
   journal={arXiv preprint arXiv:2406.19432},
   year={2024}
@@ -221,7 +221,7 @@ @article{luppi:2024
 }
 
 @article{scagliarini:2023,
-  title = {Gradients of O-information: Low-order descriptors of high-order dependencies},
+  title = {Gradients of {O}-information: Low-order descriptors of high-order dependencies},
   author = {Scagliarini, T. and Nuzzi, D. and Antonacci, Y. and Faes, L. and Rosas, F. E. and Marinazzo, D. and Stramaglia, S.},
   journal = {Phys. Rev. Res.},
   volume = {5},
@@ -236,7 +236,7 @@ @article{scagliarini:2023
 }
 
 @article{barrett:2015,
-  title = {Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems},
+  title = {Exploration of synergistic and redundant information sharing in static and dynamical {G}aussian systems},
   author = {Barrett, Adam B.},
   journal = {Phys. Rev. E},
   volume = {91},

paper/paper.md

@@ -56,9 +56,9 @@ bibliography: paper.bib
 
 # Statement of need
 
-Recent research studying higher-order interactions with information theoretic measures provides new angles and valuable insights in different fields, such as neuroscience [@gatica:2021; @herzog:2022; @combrisson:2024; @luppi:2022; @baudot:2019], music [@rosas:2019], economics [@scagliarini:2023] and psychology [@marinazzo:2022]. Information theory allows investigating higher-order interactions using a rich set of metrics that provide interpretable values of the statistical interdependency among multivariate data [@williams:2010; @mediano:2021; @barrett:2015; @rosas:2019; @scagliarini:2023; @williams:2010].
+Recent research studying higher-order interactions with information theoretic measures provides new angles and valuable insights in different fields, such as neuroscience [@gatica:2021; @herzog:2022; @combrisson:2024; @luppi:2022; @baudot:2019], music [@rosas:2019], economics [@scagliarini:2023], and psychology [@marinazzo:2022]. Information theory allows investigating higher-order interactions using a rich set of metrics that provide interpretable values of the statistical interdependency among multivariate data [@williams:2010; @mediano:2021; @barrett:2015; @rosas:2019; @scagliarini:2023; @williams:2010].
 
-Despite the relevance of studying higher-order interactions across various fields, there is currently no toolkit that compiles the latest approaches and offers user-friendly functions for calculating higher-order information metrics. Computing higher-order information presents two main challenges. First, these metrics rely on entropy and mutual information, whose estimation must be adapted to different types of data [@madukaife:2024; @czyz:2024]. Second, the computational complexity increases exponentially as the number of variables and interaction orders grows. For example, a dataset with 100 variables, has approximately 1.6e5 possible triplets, 4e6 quadruplets, and 7e7 quintuplets. Therefore, an efficient implementation, scalable on modern hardware is required.
+Despite the relevance of studying higher-order interactions across various fields, there is currently no toolkit that compiles the latest approaches and offers user-friendly functions for calculating higher-order information metrics. Computing higher-order information presents two main challenges. First, these metrics rely on entropy and mutual information, whose estimation must be adapted to different types of data [@madukaife:2024; @czyz:2024]. Second, the computational complexity increases exponentially as the number of variables and interaction orders grows. For example, a dataset with 100 variables, has approximately $1.6 \times 10^5$ possible triplets, $4 \times 10^6$ quadruplets, and $7 \times 10^7$ quintuplets. Therefore, an efficient implementation, scalable on modern hardware is required.
 
 # Related packages