diff --git a/joss.06321/10.21105.joss.06321.crossref.xml b/joss.06321/10.21105.joss.06321.crossref.xml
new file mode 100644
index 0000000000..cb026a492f
--- /dev/null
+++ b/joss.06321/10.21105.joss.06321.crossref.xml
@@ -0,0 +1,283 @@
+
+
+
+ 20240327T234716-53783c8ea0ec973b06e30e6b878e3b11d4af281c
+ 20240327234716
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Software
+ JOSS
+ 2475-9066
+
+ 10.21105/joss
+ https://joss.theoj.org
+
+
+
+
+ 03
+ 2024
+
+
+ 9
+
+ 95
+
+
+
+ TDApplied: An R package for machine learning and
+inference with persistence diagrams
+
+
+
+ Shael
+ Brown
+ https://orcid.org/0000-0001-8868-2867
+
+
+ Reza
+ Farivar-Mohseni
+ https://orcid.org/0000-0002-3123-2627
+
+
+
+ 03
+ 27
+ 2024
+
+
+ 6321
+
+
+ 10.21105/joss.06321
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.10814141
+
+
+ GitHub review issue
+ https://github.com/openjournals/joss-reviews/issues/6321
+
+
+
+ 10.21105/joss.06321
+ https://joss.theoj.org/papers/10.21105/joss.06321
+
+
+ https://joss.theoj.org/papers/10.21105/joss.06321.pdf
+
+
+
+
+
+ TDA: Statistical tools for topological data
+analysis
+ Fasy
+ 2021
+ Fasy, B. T., Kim, J., Lecci, F.,
+Maria, C., Millman, D. L., & Rouvreau., V. (2021). TDA: Statistical
+tools for topological data analysis.
+https://CRAN.R-project.org/package=TDA
+
+
+ TDAstats: Pipeline for topological data
+analysis
+ Wadhwa
+ 2019
+ Wadhwa, R., Dhawan, A., Williamson,
+D., & Scott, J. (2019). TDAstats: Pipeline for topological data
+analysis. https://github.com/rrrlw/TDAstats
+
+
+ TDAstats: R pipeline for computing persistent
+homology in topological data analysis
+ Wadhwa
+ Journal of Open Source
+Software
+ 28
+ 3
+ 10.21105/joss.00860
+ 2018
+ Wadhwa, R. R., Williamson, D. F. K.,
+Dhawan, A., & Scott, J. G. (2018). TDAstats: R pipeline for
+computing persistent homology in topological data analysis. Journal of
+Open Source Software, 3(28), 860.
+https://doi.org/10.21105/joss.00860
+
+
+ Topological persistence and
+simplification
+ Edelsbrunner
+ Discrete & Computational
+Geometry
+ 28
+ 10.1007/s00454-002-2885-2
+ 2000
+ Edelsbrunner, H., Letscher, D., &
+Zomorodian, A. (2000). Topological persistence and simplification.
+Discrete & Computational Geometry, 28, 511–533.
+https://doi.org/10.1007/s00454-002-2885-2
+
+
+ Computing persistent homology
+ Zomorodian
+ Discrete and Computational
+Geometry
+ 33
+ 10.1007/s00454-004-1146-y
+ 2005
+ Zomorodian, A., & Carlsson, G.
+(2005). Computing persistent homology. Discrete and Computational
+Geometry, 33, 249–274.
+https://doi.org/10.1007/s00454-004-1146-y
+
+
+ devtools: Tools to make developing R packages
+easier
+ Wickham
+ 2021
+ Wickham, H., Hester, J., Chang, W.,
+& Bryan, J. (2021). devtools: Tools to make developing R packages
+easier.
+https://CRAN.R-project.org/package=devtools
+
+
+ Hypothesis testing for topological data
+analysis
+ Robinson
+ Journal of Applied and Computational
+Topology
+ 1
+ 10.1007/s41468-017-0008-7
+ 2017
+ Robinson, A., & Turner, K.
+(2017). Hypothesis testing for topological data analysis. Journal of
+Applied and Computational Topology, 1.
+https://doi.org/10.1007/s41468-017-0008-7
+
+
+ Persistence Fisher kernel: A Riemannian
+manifold kernel for persistence diagrams
+ Le
+ Advances in neural information processing
+systems
+ 31
+ 10.48550/arXiv.1802.03569
+ 2018
+ Le, T., & Yamada, M. (2018).
+Persistence Fisher kernel: A Riemannian manifold kernel for persistence
+diagrams. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N.
+Cesa-Bianchi, & R. Garnett (Eds.), Advances in neural information
+processing systems (Vol. 31). Curran Associates, Inc.
+https://doi.org/10.48550/arXiv.1802.03569
+
+
+ Topological data analysis reveals robust
+alterations in the whole-brain and frontal lobe functional connectomes
+in attention-deficit/hyperactivity disorder
+ Gracia-Tabuenca
+ eneuro
+ 10.1523/eneuro.0543-19.2020
+ 2020
+ Gracia-Tabuenca, Z., Diaz-Patino, J.
+C., Arelio, I., & Alcauter, S. (2020). Topological data analysis
+reveals robust alterations in the whole-brain and frontal lobe
+functional connectomes in attention-deficit/hyperactivity disorder.
+Eneuro.
+https://doi.org/10.1523/eneuro.0543-19.2020
+
+
+ testthat: Get started with
+testing
+ Wickham
+ The R Journal
+ 3
+ 10.32614/rj-2011-002
+ 2011
+ Wickham, H. (2011). testthat: Get
+started with testing. The R Journal, 3, 5–10.
+https://doi.org/10.32614/rj-2011-002
+
+
+ Machine learning with persistent homology and
+chemical word embeddings improves prediction accuracy and
+interpretability in metal-organic frameworks
+ Krishnapriyan
+ Nature Scientific Report
+ 11
+ 10.1038/s41598-021-88027-8
+ 2021
+ Krishnapriyan, A. S. et al. (2021).
+Machine learning with persistent homology and chemical word embeddings
+improves prediction accuracy and interpretability in metal-organic
+frameworks. Nature Scientific Report, 11.
+https://doi.org/10.1038/s41598-021-88027-8
+
+
+ Unsupervised geometric and topological
+approaches for cross-lingual sentence representation and
+comparison
+ Haim Meirom
+ Proceedings of the 7th workshop on
+representation learning for NLP
+ 10.18653/v1/2022.repl4nlp-1.18
+ 2022
+ Haim Meirom, S., & Bobrowski, O.
+(2022). Unsupervised geometric and topological approaches for
+cross-lingual sentence representation and comparison. Proceedings of the
+7th Workshop on Representation Learning for NLP, 173–183.
+https://doi.org/10.18653/v1/2022.repl4nlp-1.18
+
+
+ Topological data analysis in medical imaging:
+Current state of the art
+ Singh
+ Insights into Imaging
+ 1
+ 14
+ 10.1186/s13244-023-01413-w
+ 2023
+ Singh, Y., Farrelly, C. M., Hathaway,
+Q. A., Leiner, T., Jagtap, J., Carlsson, G. E., & Erickson, B. J.
+(2023). Topological data analysis in medical imaging: Current state of
+the art. Insights into Imaging, 14(1), 58.
+https://doi.org/10.1186/s13244-023-01413-w
+
+
+ Multidimensional scaling
+ Cox
+ Handbook of data
+visualization
+ 10.1007/978-3-540-33037-0_14
+ 978-3-540-33037-0
+ 2008
+ Cox, M. A. A., & Cox, T. F.
+(2008). Multidimensional scaling. In Handbook of data visualization (pp.
+315–347). Springer Berlin Heidelberg.
+https://doi.org/10.1007/978-3-540-33037-0_14
+
+
+
+
+
+
diff --git a/joss.06321/10.21105.joss.06321.jats b/joss.06321/10.21105.joss.06321.jats
new file mode 100644
index 0000000000..759c241021
--- /dev/null
+++ b/joss.06321/10.21105.joss.06321.jats
@@ -0,0 +1,469 @@
+
+
+
+
+
+
+
+Journal of Open Source Software
+JOSS
+
+2475-9066
+
+Open Journals
+
+
+
+6321
+10.21105/joss.06321
+
+TDApplied: An R package for machine learning and
+inference with persistence diagrams
+
+
+
+https://orcid.org/0000-0001-8868-2867
+
+Brown
+Shael
+
+
+
+
+https://orcid.org/0000-0002-3123-2627
+
+Farivar-Mohseni
+Reza
+
+
+
+
+
+Department of Quantitative Life Sciences, McGill
+University, Montreal, Canada
+
+
+
+
+McGill Vision Research, Department of Opthamology, McGill
+University, Montreal, Canada
+
+
+
+
+24
+1
+2024
+
+9
+95
+6321
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2022
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+R
+topological data analysis
+persistent homology
+
+
+
+
+
+ Summary
+
Topological data analysis is a collection of tools, based on the
+ mathematical fields of topology and geometry, for finding structure in
+ whole datasets. Its main tool, persistent homology
+ (Edelsbrunner
+ et al., 2000;
+ Zomorodian
+ & Carlsson, 2005), computes a shape descriptor of a dataset
+ called a persistence diagram which encodes information about holes
+ that exist in the dataset (example applications span a variety of
+ areas, see for example Gracia-Tabuenca et al.
+ (2020),
+ Haim Meirom & Bobrowski
+ (2022),
+ and Krishnapriyan
+ (2021)).
+ These types of features cannot be identified by other methods, making
+ persistence diagrams a unique and valuable data science object for
+ studying and comparing datasets. The two most popular data science
+ tools for analyzing multiple objects are machine learning and
+ inference, but to date there has been no open source implementation of
+ published methods for machine learning and inference of persistence
+ diagrams.
+
+
+ Statement of need
+
TDApplied is the first R package for machine
+ learning and inference of persistence diagrams, building on the main R
+ packages for the calculation of persistence diagrams
+ TDA
+ (Fasy et
+ al., 2021) and TDAstats
+ (R.
+ Wadhwa et al., 2019;
+ R.
+ R. Wadhwa et al., 2018) and publications of applied analysis
+ methods for persistence diagrams
+ (Le
+ & Yamada, 2018;
+ Robinson
+ & Turner, 2017). TDApplied is
+ intended to be used by academic researchers and industry professionals
+ wanting to integrate persistence diagrams into their analysis
+ workflows. An example TDApplied workflow, in
+ which the topological differences between three datasets are
+ visualized in 2D using multidimensional scaling (MDS)
+ (Cox
+ & Cox, 2008), is visualized in
+ [fig:software]:
+
+
An example TDApplied workflow. A
+ dataset (D1, left) contains one loop (yellow) and two clusters (the
+ loop forms one cluster and the three points on the bottom are
+ another cluster, and clusters are denoted by the color red). These
+ topological features are captured with persistent homology in a
+ persistence diagram PD1 (middle top), and two other data sets, D2
+ and D3 (not shown), have their persistence diagrams, PD2 and PD3,
+ computed (middle center and middle bottom). PD1 and PD2 are not very
+ topologically different in terms of their loops, with both
+ containing a loop with similar birth and death values, and this is
+ represented by a dashed-line relationship. On the other hand, PD2
+ and PD3 are topologically different in terms of their loops because
+ PD3 does not contain a loop, and this is represented by a
+ dotted-line relationship. TDApplied can
+ quantify these topological differences and use MDS to project the
+ persistence diagrams into three points in a 2D embedding space
+ (right) where interpoint distances reflect the topological
+ differences between the persistence diagrams.
+
+
+
+
The TDApplied package is built on three main
+ pillars:
+
+
+
User-friendly – internal preprocessing of persistence diagrams
+ that would normally be left to R users to figure out ad hoc, and
+ functions designed to easily flow from input diagrams to output
+ metrics.
+
+
+
Efficient – parallelization, C code, computational tricks and
+ storage of reusable and cumbersome calculations significantly
+ increases the feasibility of topological analyses (compared to
+ existing R packages).
+
+
+
Flexible – ability to interface with other data science
+ packages to create personalized analyses.
+
+
+
TDApplied has already been featured in a
+ conference
+ workshop and a
+ conference
+ tutorial, utilized in a journal publication
+ (Singh
+ et al., 2023) and downloaded over 4400 times. Therefore, we
+ propose TDApplied as a user-friendly, efficient
+ and flexible R package for the analysis of multiple datasets using
+ machine learning and inference via topological data analysis.
+
+
+ Project Management
+
Installation and availability:
+ TDApplied can be installed directly from CRAN
+ using the command
+ install.packages("TDApplied"), or
+ from GitHub using the devtools package
+ (Wickham
+ et al., 2021). TDApplied is distributed
+ under the GPL-3 license.
+
Code quality: Code has been tested using the
+ testthat package
+ (Wickham,
+ 2011), with 91.45% coverage of R code when not skipping tests
+ involving Python code (or 88.44% coverage when skipping the Python
+ tests).
+
Documentation:TDApplied
+ contains five main vignettes:
+
+
+
“TDApplied Theory and Practice” provides example function usage
+ on simulated data as well as mathematical background and
+ intuition,
+
+
+
“Human Connectome Project Analysis” demonstrates an applied
+ example analysis of neurological data,
+
+
+
“Benchmarking and Speedups” outlines the package’s optimization
+ strategies and highlights performance gains compared to other
+ packages,
+
+
+
“Personalized Analyses with TDApplied” demonstrates how to
+ interface TDApplied with other data science
+ packages, and
+
+
+
“Comparing Distance Calculations” accounts for differences in
+ computed distance values between persistence diagrams across
+ comparable packages.
+
+
+
+
+ Acknowledgements
+
We acknowledge funding from the CIHR 2016 grant for cortical
+ mechanisms of 3-D scene and object recognition in the primate
+ brain.
+
+
+
+
+
+
+
+ FasyBrittany T.
+ KimJisu
+ LecciFabrizio
+ MariaClement
+ MillmanDavid L.
+ Rouvreau.Vincent
+
+ TDA: Statistical tools for topological data analysis
+ 2021
+ https://CRAN.R-project.org/package=TDA
+
+
+
+
+
+ WadhwaRaoul
+ DhawanAndrew
+ WilliamsonDrew
+ ScottJacob
+
+ TDAstats: Pipeline for topological data analysis
+ 2019
+ https://github.com/rrrlw/TDAstats
+
+
+
+
+
+ WadhwaRaoul R.
+ WilliamsonDrew F. K.
+ DhawanAndrew
+ ScottJacob G.
+
+ TDAstats: R pipeline for computing persistent homology in topological data analysis
+ Journal of Open Source Software
+ 2018
+ 3
+ 28
+ https://doi.org/10.21105/joss.00860
+ 10.21105/joss.00860
+ 860
+
+
+
+
+
+
+ EdelsbrunnerHerbert
+ LetscherDavid
+ ZomorodianAfra
+
+ Topological persistence and simplification
+ Discrete & Computational Geometry
+ 2000
+ 28
+ 10.1007/s00454-002-2885-2
+ 511
+ 533
+
+
+
+
+
+ ZomorodianAfra
+ CarlssonGunnar
+
+ Computing persistent homology
+ Discrete and Computational Geometry
+ 200502
+ 33
+ 10.1007/s00454-004-1146-y
+ 249
+ 274
+
+
+
+
+
+ WickhamHadley
+ HesterJim
+ ChangWinston
+ BryanJennifer
+
+ devtools: Tools to make developing R packages easier
+ 2021
+ https://CRAN.R-project.org/package=devtools
+
+
+
+
+
+ RobinsonAndrew
+ TurnerKatharine
+
+ Hypothesis testing for topological data analysis
+ Journal of Applied and Computational Topology
+ 2017
+ 1
+ 10.1007/s41468-017-0008-7
+
+
+
+
+
+ LeTam
+ YamadaMakoto
+
+ Persistence Fisher kernel: A Riemannian manifold kernel for persistence diagrams
+ Advances in neural information processing systems
+
+ BengioS.
+ WallachH.
+ LarochelleH.
+ GraumanK.
+ Cesa-BianchiN.
+ GarnettR.
+
+ Curran Associates, Inc.
+ 2018
+ 31
+ https://proceedings.neurips.cc/paper/2018/file/959ab9a0695c467e7caf75431a872e5c-Paper.pdf
+ 10.48550/arXiv.1802.03569
+
+
+
+
+
+
+
+ Gracia-TabuencaZeus
+ Diaz-PatinoJuan Carlos
+ ArelioIsaac
+ AlcauterSarael
+
+ Topological data analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in attention-deficit/hyperactivity disorder
+ eneuro
+ 2020
+ 10.1523/eneuro.0543-19.2020
+
+
+
+
+
+ WickhamHadley
+
+ testthat: Get started with testing
+ The R Journal
+ 2011
+ 3
+ https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf
+ 10.32614/rj-2011-002
+ 5
+ 10
+
+
+
+
+
+ KrishnapriyanAditi S. et al
+
+ Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
+ Nature Scientific Report
+ 202104
+ 11
+ 10.1038/s41598-021-88027-8
+
+
+
+
+
+
+
+ Haim MeiromShaked
+ BobrowskiOmer
+
+ Unsupervised geometric and topological approaches for cross-lingual sentence representation and comparison
+ Proceedings of the 7th workshop on representation learning for NLP
+ Association for Computational Linguistics
+ Dublin, Ireland
+ 202205
+ https://aclanthology.org/2022.repl4nlp-1.18
+ 10.18653/v1/2022.repl4nlp-1.18
+ 173
+ 183
+
+
+
+
+
+ SinghYashbir
+ FarrellyColleen M.
+ HathawayQuincy A.
+ LeinerTim
+ JagtapJaidip
+ CarlssonGunnar E.
+ EricksonBradley J.
+
+ Topological data analysis in medical imaging: Current state of the art
+ Insights into Imaging
+ 2023
+ 14
+ 1
+ 10.1186/s13244-023-01413-w
+ 58
+
+
+
+
+
+
+ CoxMichael A. A.
+ CoxTrevor F.
+
+ Multidimensional scaling
+ Handbook of data visualization
+ Springer Berlin Heidelberg
+ Berlin, Heidelberg
+ 2008
+ 978-3-540-33037-0
+ https://doi.org/10.1007/978-3-540-33037-0_14
+ 10.1007/978-3-540-33037-0_14
+ 315
+ 347
+
+
+
+
+
diff --git a/joss.06321/10.21105.joss.06321.pdf b/joss.06321/10.21105.joss.06321.pdf
new file mode 100644
index 0000000000..5b8dfe891a
Binary files /dev/null and b/joss.06321/10.21105.joss.06321.pdf differ
diff --git a/joss.06321/media/software.pdf b/joss.06321/media/software.pdf
new file mode 100644
index 0000000000..b3758b2964
Binary files /dev/null and b/joss.06321/media/software.pdf differ