From 45f68ce0210a0fb308305f5c1119a24828d48ea4 Mon Sep 17 00:00:00 2001 From: Matteo Delucchi Date: Mon, 30 Sep 2024 08:44:33 +0200 Subject: [PATCH] Fixed capitalization in references --- vignettes/paper.bib | 38 ++++++++++++++++++-------------------- 1 file changed, 18 insertions(+), 20 deletions(-) diff --git a/vignettes/paper.bib b/vignettes/paper.bib index 9674601d..89f5efac 100644 --- a/vignettes/paper.bib +++ b/vignettes/paper.bib @@ -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}, @@ -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.}, @@ -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}, @@ -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/}, @@ -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}, @@ -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}, @@ -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}, @@ -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}, @@ -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, @@ -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}, @@ -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}, @@ -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}, @@ -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}, @@ -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}, @@ -357,8 +356,7 @@ @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}, @@ -366,7 +364,7 @@ @Manual{rcpparmadillo2023 } @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}, @@ -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}, @@ -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},