JOSS Paper content

.github/workflows/build-joss-paper.yml

@@ -0,0 +1,27 @@
+name: Build a pdf for the paper
+
+on:
+  push:
+    branches: [JOSSpaper]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@1d5c3be74a6a8454854f099d63c6fbb1e8938052
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: ./vignettes/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: ./vignettes/paper.pdf

vignettes/paper.bib

@@ -117,6 +117,133 @@ @article{kratzer_additive_2023
 	file = {Full Text PDF:/home/matteo/Zotero/storage/4HPJLAUD/Kratzer et al. - 2023 - Additive Bayesian Network Modeling with the R Pack.pdf:application/pdf},
 }
 
+@article{kalisch_causal_2012,
+	title = {Causal Inference Using Graphical Models with the R Package pcalg},
+	volume = {47},
+	rights = {Copyright (c) 2010 Markus Kalisch, Martin Mächler, Diego Colombo, Marloes H. Maathuis, Peter Bühlmann},
+	issn = {1548-7660},
+	url = {https://www.jstatsoft.org/index.php/jss/article/view/v047i11},
+	doi = {10.18637/jss.v047.i11},
+	pages = {1--26},
+	number = {1},
+	journaltitle = {Journal of Statistical Software},
+	author = {Kalisch, Markus and Mächler, Martin and Colombo, Diego and Maathuis, Marloes H. and Bühlmann, Peter},
+	urldate = {2021-04-19},
+	date = {2012-05-17},
+	langid = {english},
+	note = {Number: 1},
+	file = {Full Text:/home/matteo/Zotero/storage/WMY63N9G/Kalisch et al. - 2012 - Causal Inference Using Graphical Models with the R.pdf:application/pdf;Kalisch et al. - 2012 - Causal Inference Using Graphical Models with the .pdf:/home/matteo/Zotero/storage/YPZMUHFF/Kalisch et al. - 2012 - Causal Inference Using Graphical Models with the .pdf:application/pdf;Snapshot:/home/matteo/Zotero/storage/JZHLD22K/v047i11.html:text/html},
+}
+
+@article{boettcher_deal_2003,
+	title = {deal: A Package for Learning Bayesian Networks},
+	volume = {8},
+	rights = {Copyright (c) 2003 Susanne G. Boettcher, Claus Dethlefsen},
+	issn = {1548-7660},
+	url = {https://doi.org/10.18637/jss.v008.i20},
+	doi = {10.18637/jss.v008.i20},
+	shorttitle = {deal},
+	abstract = {deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.},
+	pages = {1--40},
+	journaltitle = {Journal of Statistical Software},
+	author = {Boettcher, Susanne G. and Dethlefsen, Claus},
+	urldate = {2024-03-26},
+	date = {2003-12-28},
+	langid = {english},
+	file = {Submitted Version:/home/matteo/Zotero/storage/YN3JDDFW/Boettcher and Dethlefsen - 2003 - deal A Package for Learning Bayesian Networks.pdf:application/pdf},
+}
+
+@article{franzin_bnstruct_2017,
+	title = {bnstruct: an R package for Bayesian Network structure learning in the presence of missing data},
+	volume = {33},
+	issn = {1367-4803},
+	url = {https://doi.org/10.1093/bioinformatics/btw807},
+	doi = {10.1093/bioinformatics/btw807},
+	shorttitle = {bnstruct},
+	abstract = {A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks, particularly the manipulation of missing data, which is a common situation in practice.The software is implemented in R and C and is available on {CRAN} under a {GPL} licence.Supplementary data are available at Bioinformatics online.},
+	pages = {1250--1252},
+	number = {8},
+	journaltitle = {Bioinformatics},
+	shortjournal = {Bioinformatics},
+	author = {Franzin, Alberto and Sambo, Francesco and Di Camillo, Barbara},
+	urldate = {2024-03-26},
+	date = {2017-04-15},
+	file = {Full Text PDF:/home/matteo/Zotero/storage/U7DDDXPX/Franzin et al. - 2017 - bnstruct an R package for Bayesian Network struct.pdf:application/pdf},
+}
+
+@article{hojsgaard_graphical_2012,
+	title = {Graphical Independence Networks with the {gRain} Package for R},
+	volume = {46},
+	rights = {Copyright (c) 2009 Søren  Højsgaard},
+	issn = {1548-7660},
+	url = {https://doi.org/10.18637/jss.v046.i10},
+	doi = {10.18637/jss.v046.i10},
+	abstract = {In this paper we present the R package {gRain} for propagation in graphical independence networks (for which Bayesian networks is a special instance). The paper includes a description of the theory behind the computations. The main part of the paper is an illustration of how to use the package. The paper also illustrates how to turn a graphical model and data into an independence network.},
+	pages = {1--26},
+	journaltitle = {Journal of Statistical Software},
+	author = {Højsgaard, Søren},
+	urldate = {2024-03-26},
+	date = {2012-02-28},
+	langid = {english},
+	file = {Højsgaard - 2012 - Graphical Independence Networks with the gRain Pac.pdf:/home/matteo/Zotero/storage/NVI3D9SB/Højsgaard - 2012 - Graphical Independence Networks with the gRain Pac.pdf:application/pdf},
+}
+
+@article{tsagris_new_2021,
+	title = {A New Scalable Bayesian Network Learning Algorithm with Applications to Economics},
+	volume = {57},
+	issn = {1572-9974},
+	url = {https://doi.org/10.1007/s10614-020-10065-7},
+	doi = {10.1007/s10614-020-10065-7},
+	abstract = {This paper proposes a new Bayesian network learning algorithm, termed {PCHC}, that is designed to work with either continuous or categorical data. {PCHC} is a hybrid algorithm that consists of the skeleton identification phase (learning the relationships among the variables) followed by the scoring phase that assigns the causal directions. Monte Carlo simulations clearly show that {PCHC} is dramatically faster, enjoys a nice scalability with respect to the sample size, and produces Bayesian networks of similar to, or of higher accuracy than, a competing state of the art hybrid algorithm. {PCHC} is finally applied to real data illustrating its performance and advantages.},
+	pages = {341--367},
+	number = {1},
+	journaltitle = {Computational Economics},
+	shortjournal = {Comput Econ},
+	author = {Tsagris, Michail},
+	urldate = {2024-03-26},
+	date = {2021-01-01},
+	langid = {english},
+	keywords = {Bayesian networks, Causality, Economics data},
+	file = {Full Text PDF:/home/matteo/Zotero/storage/FGN55RHT/Tsagris - 2021 - A New Scalable Bayesian Network Learning Algorithm.pdf:application/pdf},
+}
+
+@article{zanga_survey_2022,
+	title = {A Survey on Causal Discovery: Theory and Practice},
+	volume = {151},
+	issn = {0888-613X},
+	url = {https://www.sciencedirect.com/science/article/pii/S0888613X22001402},
+	doi = {10.1016/j.ijar.2022.09.004},
+	shorttitle = {A Survey on Causal Discovery},
+	abstract = {Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.},
+	pages = {101--129},
+	journaltitle = {International Journal of Approximate Reasoning},
+	shortjournal = {International Journal of Approximate Reasoning},
+	author = {Zanga, Alessio and Ozkirimli, Elif and Stella, Fabio},
+	urldate = {2024-03-26},
+	date = {2022-12-01},
+	keywords = {Causal discovery, Causal models, Causality, Structural learning},
+	file = {Submitted Version:/home/matteo/Zotero/storage/54VIJP64/Zanga et al. - 2022 - A Survey on Causal Discovery Theory and Practice.pdf:application/pdf},
+}
+
+@article{kitson_survey_2023,
+	title = {A survey of Bayesian Network structure learning},
+	volume = {56},
+	issn = {1573-7462},
+	url = {https://doi.org/10.1007/s10462-022-10351-w},
+	doi = {10.1007/s10462-022-10351-w},
+	abstract = {Bayesian Networks ({BNs}) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-world areas where we seek to answer complex questions based on hypothetical evidence to determine actions for intervention. However, determining the graphical structure of a {BN} remains a major challenge, especially when modelling a problem under causal assumptions. Solutions to this problem include the automated discovery of {BN} graphs from data, constructing them based on expert knowledge, or a combination of the two. This paper provides a comprehensive review of combinatoric algorithms proposed for learning {BN} structure from data, describing 74 algorithms including prototypical, well-established and state-of-the-art approaches. The basic approach of each algorithm is described in consistent terms, and the similarities and differences between them highlighted. Methods of evaluating algorithms and their comparative performance are discussed including the consistency of claims made in the literature. Approaches for dealing with data noise in real-world datasets and incorporating expert knowledge into the learning process are also covered.},
+	pages = {8721--8814},
+	number = {8},
+	journaltitle = {Artificial Intelligence Review},
+	shortjournal = {Artif Intell Rev},
+	author = {Kitson, Neville Kenneth and Constantinou, Anthony C. and Guo, Zhigao and Liu, Yang and Chobtham, Kiattikun},
+	urldate = {2024-03-20},
+	date = {2023-08-01},
+	langid = {english},
+	keywords = {Causal discovery, Graphical models, Knowledge-based constraints, Structure learning evaluation, Structure learning review},
+	file = {Full Text PDF:/home/matteo/Zotero/storage/E9QZ3GQ4/Kitson et al. - 2023 - A survey of Bayesian Network structure learning.pdf:application/pdf},
+}
+
 @Manual{rcore2024,
   title = {R: A Language and Environment for Statistical Computing},
   author = {{R Core Team}},