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152 paper submission next steps (#157)
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* fixed capitalization in references, addressing #153.

* more capitalization in references, addressing #153.

* build joss paper also on this branch
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matteodelucchi authored Sep 18, 2024
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1 change: 1 addition & 0 deletions .github/workflows/build-joss-paper.yml
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Expand Up @@ -3,6 +3,7 @@ name: Build JOSS paper pdf
on:
push:
branches:
- 152-paper-submission-next-steps
- JOSSpaper_noT
- JOSSpaper
- main
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36 changes: 18 additions & 18 deletions vignettes/paper.bib
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Expand Up @@ -20,7 +20,7 @@ @article{kratzer_information-theoretic_2018
title = {Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology},
url = {http://arxiv.org/abs/1808.01126},
doi = {10.48550/arXiv.1808.01126},
abstract = {Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e.g., systems epidemiology. A popular approach to learn a Bayesian network from an observational datasets is to identify the maximum a posteriori network in a search-and-score approach. Many scores have been proposed both Bayesian or frequentist based. In an applied perspective, a suitable approach would allow multiple distributions for the data and is robust enough to run autonomously. A promising framework to compute scores are generalized linear models. Indeed, there exists fast algorithms for estimation and many tailored solutions to common epidemiological issues. The purpose of this paper is to present an R package abn that has an implementation of multiple frequentist scores and some realistic simulations that show its usability and performance. It includes features to deal efficiently with data separation and adjustment which are very common in systems epidemiology.},
abstract = {Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e.g., systems epidemiology. A popular approach to learn a Bayesian network from an observational datasets is to identify the maximum a posteriori network in a search-and-score approach. Many scores have been proposed both Bayesian or frequentist based. In an applied perspective, a suitable approach would allow multiple distributions for the data and is robust enough to run autonomously. A promising framework to compute scores are generalized linear models. Indeed, there exists fast algorithms for estimation and many tailored solutions to common epidemiological issues. The purpose of this paper is to present an R package {abn} that has an implementation of multiple frequentist scores and some realistic simulations that show its usability and performance. It includes features to deal efficiently with data separation and adjustment which are very common in systems epidemiology.},
journaltitle = {{arXiv}:1808.01126 [cs, stat]},
author = {Kratzer, Gilles and Furrer, Reinhard},
urldate = {2021-08-20},
Expand Down Expand Up @@ -101,13 +101,13 @@ @article{delucchi_bayesian_2022
}

@article{kratzer_additive_2023,
title = {Additive Bayesian Network Modeling with the R Package abn},
title = {Additive Bayesian Network Modeling with the {R} Package {abn}},
volume = {105},
rights = {Copyright (c) 2023 Gilles Kratzer, Fraser I. Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer},
issn = {1548-7660},
url = {https://doi.org/10.18637/jss.v105.i08},
doi = {10.18637/jss.v105.i08},
abstract = {The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production.},
abstract = {The R package {abn} is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production.},
pages = {1--41},
journaltitle = {Journal of Statistical Software},
author = {Kratzer, Gilles and Lewis, Fraser I. and Comin, Arianna and Pittavino, Marta and Furrer, Reinhard},
Expand All @@ -119,7 +119,7 @@ @article{kratzer_additive_2023
}

@article{kalisch_causal_2012,
title = {Causal Inference Using Graphical Models with the R Package pcalg},
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},
Expand Down Expand Up @@ -155,7 +155,7 @@ @article{boettcher_deal_2003
}

@article{franzin_bnstruct_2017,
title = {bnstruct: an R package for Bayesian Network structure learning in the presence of missing data},
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},
Expand All @@ -169,11 +169,11 @@ @article{franzin_bnstruct_2017
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},
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},
title = {Graphical Independence Networks with the {gRain} Package for {R}},
volume = {46},
rights = {Copyright (c) 2009 Søren Højsgaard},
issn = {1548-7660},
Expand Down Expand Up @@ -246,7 +246,7 @@ @article{kitson_survey_2023
}

@Manual{rcore2024,
title = {R: A Language and Environment for Statistical Computing},
title = {{R}: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
Expand All @@ -265,15 +265,15 @@ @Article{bnlearn2010
}

@Manual{rjags2022,
title = {rjags: Bayesian Graphical Models using MCMC},
title = {rjags: Bayesian Graphical Models using {MCMC}},
author = {Martyn Plummer},
year = {2022},
note = {R package version 4-13},
url = {https://CRAN.R-project.org/package=rjags},
}

@Book{nnet2002,
title = {Modern Applied Statistics with S},
title = {Modern Applied Statistics with {S}},
author = {W. N. Venables and B. D. Ripley},
publisher = {Springer},
edition = {Fourth},
Expand All @@ -295,7 +295,7 @@ @Article{lme42015
}

@Manual{graph2022,
title = {graph: graph: A package to handle graph data structures},
title = {{graph}: A package to handle graph data structures},
author = {R Gentleman and Elizabeth Whalen and W Huber and S Falcon},
year = {2022},
note = {R package version 1.76.0},
Expand All @@ -304,7 +304,7 @@ @Manual{graph2022
}

@Manual{rgraphviz2022,
title = {Rgraphviz: Provides plotting capabilities for R graph objects},
title = {{Rgraphviz}: Provides plotting capabilities for {R} graph objects},
author = {Kasper Daniel Hansen and Jeff Gentry and Li Long and Robert Gentleman and Seth Falcon and Florian Hahne and Deepayan Sarkar},
year = {2022},
note = {R package version 2.42.0},
Expand All @@ -313,23 +313,23 @@ @Manual{rgraphviz2022
}

@Manual{doparallel2022,
title = {doParallel: Foreach Parallel Adaptor for the 'parallel' Package},
title = {{doParallel}: Foreach Parallel Adaptor for the 'parallel' Package},
author = {Microsoft Corporation and Steve Weston},
year = {2022},
note = {R package version 1.0.17},
url = {https://CRAN.R-project.org/package=doParallel},
}

@Manual{foreach2022,
title = {foreach: Provides Foreach Looping Construct},
title = {{foreach}: Provides Foreach Looping Construct},
author = {{Microsoft} and Steve Weston},
year = {2022},
note = {R package version 1.5.2},
url = {https://CRAN.R-project.org/package=foreach},
}

@Manual{mclogit2022,
title = {mclogit: Multinomial Logit Models, with or without Random Effects or
title = {{mclogit}: Multinomial Logit Models, with or without Random Effects or
Overdispersion},
author = {Martin Elff},
year = {2022},
Expand All @@ -349,15 +349,15 @@ @Article{stringi2022
}

@Manual{rcpp2023,
title = {Rcpp: Seamless R and C++ Integration},
title = {{Rcpp}: Seamless {R} and {C++} Integration},
author = {Dirk Eddelbuettel and Romain Francois and JJ Allaire and Kevin Ushey and Qiang Kou and Nathan Russell and Inaki Ucar and Douglas Bates and John Chambers},
year = {2023},
note = {R package version 1.0.11},
url = {https://CRAN.R-project.org/package=Rcpp},
}

@Manual{rcpparmadillo2023,
title = {RcppArmadillo: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra
title = {{RcppArmadillo}: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra
Library},
author = {Dirk Eddelbuettel and Romain Francois and Doug Bates and Binxiang Ni and Conrad Sanderson},
year = {2023},
Expand Down Expand Up @@ -388,7 +388,7 @@ @software{galassi_gnu_2021
@inproceedings{plummer_jags_2003,
location = {Vienna, Austria},
title = {{JAGS}: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling},
abstract = {{JAGS} is a program for Bayesian Graphical modelling which aims for compatibility with classic {BUGS}. The program could eventually be developed as an R package. This article explains the motivations for this program, briefly describes the architecture and then discusses some ideas for a vectorized form of the {BUGS} language.},
abstract = {{JAGS} is a program for Bayesian Graphical modelling which aims for compatibility with classic {BUGS}. The program could eventually be developed as an {R} package. This article explains the motivations for this program, briefly describes the architecture and then discusses some ideas for a vectorized form of the {BUGS} language.},
eventtitle = {{DSC} 2003},
pages = {1--10},
booktitle = {Proceedings of the 3rd International Workshop on Distributed Statistical Computing},
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