34  Librairies de code R utilisées dans ce livre

Ce livre n’aurait pas été possible sans la contribution immense de la communauté R et des extraordinaires librairies de code qu’elle produit.

Voici la liste de toutes les librairies utilisées dans ce livre, soit dans les laboratoires, ou pour la création du livre comme tel :

Package	Version	Citation
base	4.4.0	R Core Team (2024)
car	3.1.2	Fox et Weisberg (2019)
GGally	2.2.1	Schloerke et al. (2024)
ggdendro	0.2.0	de Vries et Ripley (2024)
ggforce	0.4.2	Pedersen (2024)
ggthemes	5.1.0	Arnold (2024)
grateful	0.2.4	Francisco Rodriguez-Sanchez et Connor P. Jackson (2023)
knitr	1.46	Xie (2014); Xie (2015); Xie (2024)
leaps	3.1	Fortran code by Alan Miller (2020)
mgcv	1.9.1	S. N. Wood (2003); S. N. Wood (2004); S. N. Wood (2011); S. N. Wood et al. (2016); S. N. Wood (2017)
MuMIn	1.47.5	Bartoń (2023)
nlme	3.1.164	J. C. Pinheiro et Bates (2000); J. Pinheiro, Bates, et R Core Team (2023)
palmerpenguins	0.1.1	Horst, Hill, et Gorman (2020)
rmarkdown	2.27	Xie, Allaire, et Grolemund (2018); Xie, Dervieux, et Riederer (2020); Allaire et al. (2024)
tidyverse	2.0.0	Wickham et al. (2019)
tree	1.0.43	Ripley (2023)
vegan	2.6.6.1	Oksanen et al. (2024)
visreg	2.7.0	Breheny et Burchett (2017)

Cette liste de librairies a été générée automatiquement par la librairie grateful.

Allaire, JJ, Yihui Xie, Christophe Dervieux, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, et al. 2024. rmarkdown: Dynamic Documents for R. https://github.com/rstudio/rmarkdown.

Arnold, Jeffrey B. 2024. ggthemes: Extra Themes, Scales and Geoms for « ggplot2 ». https://CRAN.R-project.org/package=ggthemes.

Bartoń, Kamil. 2023. MuMIn: Multi-Model Inference. https://CRAN.R-project.org/package=MuMIn.

Breheny, Patrick, et Woodrow Burchett. 2017. « Visualization of Regression Models Using visreg ». The R Journal 9 (2): 56‑71.

de Vries, Andrie, et Brian D. Ripley. 2024. ggdendro: Create Dendrograms and Tree Diagrams Using « ggplot2 ». https://CRAN.R-project.org/package=ggdendro.

Fortran code by Alan Miller, Thomas Lumley based on. 2020. leaps: Regression Subset Selection. https://CRAN.R-project.org/package=leaps.

Fox, John, et Sanford Weisberg. 2019. An R Companion to Applied Regression. Third. Thousand Oaks CA: Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.

Francisco Rodriguez-Sanchez, et Connor P. Jackson. 2023. grateful: Facilitate citation of R packages. https://pakillo.github.io/grateful/.

Horst, Allison Marie, Alison Presmanes Hill, et Kristen B Gorman. 2020. palmerpenguins: Palmer Archipelago (Antarctica) penguin data. https://doi.org/10.5281/zenodo.3960218.

Oksanen, Jari, Gavin L. Simpson, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter R. Minchin, R. B. O’Hara, et al. 2024. vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan.

Pedersen, Thomas Lin. 2024. ggforce: Accelerating « ggplot2 ». https://CRAN.R-project.org/package=ggforce.

Pinheiro, José C., et Douglas M. Bates. 2000. Mixed-Effects Models in S and S-PLUS. New York: Springer. https://doi.org/10.1007/b98882.

Pinheiro, José, Douglas Bates, et R Core Team. 2023. nlme: Linear and Nonlinear Mixed Effects Models. https://CRAN.R-project.org/package=nlme.

R Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Ripley, Brian. 2023. tree: Classification and Regression Trees. https://CRAN.R-project.org/package=tree.

Schloerke, Barret, Di Cook, Joseph Larmarange, Francois Briatte, Moritz Marbach, Edwin Thoen, Amos Elberg, et Jason Crowley. 2024. GGally: Extension to « ggplot2 ». https://CRAN.R-project.org/package=GGally.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. « Welcome to the tidyverse ». Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.

Wood, S. N. 2017. Generalized Additive Models: An Introduction with R. 2ᵉ éd. Chapman; Hall/CRC.

Wood, S. N. 2003. « Thin-plate regression splines ». Journal of the Royal Statistical Society (B) 65 (1): 95‑114.

———. 2004. « Stable and efficient multiple smoothing parameter estimation for generalized additive models ». Journal of the American Statistical Association 99 (467): 673‑86.

———. 2011. « Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models ». Journal of the Royal Statistical Society (B) 73 (1): 3‑36.

Wood, S. N., N., Pya, et B. S"afken. 2016. « Smoothing parameter and model selection for general smooth models (with discussion) ». Journal of the American Statistical Association 111: 1548‑75.

Xie, Yihui. 2014. « knitr: A Comprehensive Tool for Reproducible Research in R ». In Implementing Reproducible Computational Research, édité par Victoria Stodden, Friedrich Leisch, et Roger D. Peng. Chapman; Hall/CRC.

———. 2015. Dynamic Documents with R and knitr. 2nd éd. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.

———. 2024. knitr: A General-Purpose Package for Dynamic Report Generation in R. https://yihui.org/knitr/.

Xie, Yihui, J. J. Allaire, et Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown.

Xie, Yihui, Christophe Dervieux, et Emily Riederer. 2020. R Markdown Cookbook. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown-cookbook.