Skip to content

Commit

Permalink
Fixed capitalization in references
Browse files Browse the repository at this point in the history
  • Loading branch information
@matteodelucchi
matteodelucchi committed Sep 30, 2024
1 parent 7f3e909 commit 45f68ce
Showing 1 changed file with 18 additions and 20 deletions.
38 changes: 18 additions & 20 deletions vignettes/paper.bib
View file
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@

@article{kratzer_bayesian_2020,
title = {Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland},
title = {{B}ayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in {S}witzerland},
volume = {7},
issn = {2297-1769},
url = {https://www.frontiersin.org/articles/10.3389/fvets.2020.00073/full},
Expand All @@ -17,7 +17,7 @@ @article{kratzer_bayesian_2020
}

@article{kratzer_information-theoretic_2018,
title = {Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology},
title = {Information-Theoretic Scoring Rules to Learn Additive {B}ayesian 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.},
Expand All @@ -32,7 +32,7 @@ @article{kratzer_information-theoretic_2018
}

@article{pittavino_comparison_2017,
title = {Comparison between generalized linear modelling and additive Bayesian network; identification of factors associated with the incidence of antibodies against Leptospira interrogans sv Pomona in meat workers in New Zealand},
title = {Comparison between generalized linear modelling and additive {B}ayesian network; identification of factors associated with the incidence of antibodies against {L}eptospira interrogans sv {P}omona in meat workers in {N}ew {Z}ealand},
volume = {173},
issn = {0001-706X},
url = {https://www.sciencedirect.com/science/article/pii/S0001706X16308828},
Expand All @@ -55,7 +55,7 @@ @article{pittavino_comparison_2017
}

@article{hartnack_additive_2019,
title = {Additive Bayesian networks for antimicrobial resistance and potential risk factors in non-typhoidal Salmonella isolates from layer hens in Uganda},
title = {Additive {B}ayesian networks for antimicrobial resistance and potential risk factors in non-typhoidal {S}almonella isolates from layer hens in {U}ganda},
volume = {15},
issn = {1746-6148},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591809/},
Expand Down Expand Up @@ -83,7 +83,7 @@ @article{hartnack_additive_2019
}

@article{delucchi_bayesian_2022,
title = {Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors},
title = {{B}ayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors},
volume = {147},
issn = {0010-4825},
url = {https://www.sciencedirect.com/science/article/pii/S0010482522005133},
Expand All @@ -101,7 +101,7 @@ @article{delucchi_bayesian_2022
}

@article{kratzer_additive_2023,
title = {Additive Bayesian Network Modeling with the {R} Package {abn}},
title = {Additive {B}ayesian 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},
Expand Down Expand Up @@ -137,7 +137,7 @@ @article{kalisch_causal_2012
}

@article{boettcher_deal_2003,
title = {deal: A Package for Learning Bayesian Networks},
title = {{deal}: A Package for Learning {B}ayesian Networks},
volume = {8},
rights = {Copyright (c) 2003 Susanne G. Boettcher, Claus Dethlefsen},
issn = {1548-7660},
Expand All @@ -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 {B}ayesian Network structure learning in the presence of missing data},
volume = {33},
issn = {1367-4803},
url = {https://doi.org/10.1093/bioinformatics/btw807},
Expand All @@ -169,7 +169,7 @@ @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 {B}ayesian Network struct.pdf:application/pdf},
}

@article{hojsgaard_graphical_2012,
Expand All @@ -190,7 +190,7 @@ @article{hojsgaard_graphical_2012
}

@article{tsagris_new_2021,
title = {A New Scalable Bayesian Network Learning Algorithm with Applications to Economics},
title = {A New Scalable {B}ayesian Network Learning Algorithm with Applications to Economics},
volume = {57},
issn = {1572-9974},
url = {https://doi.org/10.1007/s10614-020-10065-7},
Expand Down Expand Up @@ -227,7 +227,7 @@ @article{zanga_survey_2022
}

@article{kitson_survey_2023,
title = {A survey of Bayesian Network structure learning},
title = {A survey of {B}ayesian Network structure learning},
volume = {56},
issn = {1573-7462},
url = {https://doi.org/10.1007/s10462-022-10351-w},
Expand Down Expand Up @@ -255,7 +255,7 @@ @Manual{rcore2024
}

@Article{bnlearn2010,
title = {Learning Bayesian Networks with the {bnlearn} {R} Package},
title = {Learning {B}ayesian Networks with the {bnlearn} {R} Package},
author = {Marco Scutari},
journal = {Journal of Statistical Software},
year = {2010},
Expand All @@ -265,7 +265,7 @@ @Article{bnlearn2010
}

@Manual{rjags2022,
title = {rjags: Bayesian Graphical Models using {MCMC}},
title = {{rjags}: {B}ayesian Graphical Models using {MCMC}},
author = {Martyn Plummer},
year = {2022},
note = {R package version 4-13},
Expand Down Expand Up @@ -329,8 +329,7 @@ @Manual{foreach2022
}

@Manual{mclogit2022,
title = {{mclogit}: Multinomial Logit Models, with or without Random Effects or
Overdispersion},
title = {{mclogit}: Multinomial Logit Models, with or without Random Effects or Overdispersion},
author = {Martin Elff},
year = {2022},
note = {R package version 0.9.6},
Expand All @@ -357,16 +356,15 @@ @Manual{rcpp2023
}

@Manual{rcpparmadillo2023,
title = {{RcppArmadillo}: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra
Library},
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},
note = {R package version 0.12.6.6.1},
url = {https://CRAN.R-project.org/package=RcppArmadillo},
}

@Article{inla2013,
title = {Bayesian computing with {INLA}: {N}ew features.},
title = {{B}ayesian computing with {INLA}: {N}ew features.},
doi = {10.1016/j.csda.2013.04.014},
author = {Thiago G. Martins and Daniel Simpson and Finn Lindgren and H{\aa}vard Rue},
year = {2013},
Expand All @@ -387,7 +385,7 @@ @software{galassi_gnu_2021

@inproceedings{plummer_jags_2003,
location = {Vienna, Austria},
title = {{JAGS}: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling},
title = {{JAGS}: A Program for Analysis of {B}ayesian Graphical Models Using {G}ibbs 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.},
eventtitle = {{DSC} 2003},
pages = {1--10},
Expand All @@ -400,7 +398,7 @@ @inproceedings{plummer_jags_2003

@Article{testthat2011,
author = {Hadley Wickham},
title = {testthat: Get Started with Testing},
title = {{testthat}: Get Started with Testing},
doi = {10.32614/rj-2011-002},
journal = {The R Journal},
year = {2011},
Expand Down

0 comments on commit 45f68ce

Please sign in to comment.