To run these, you need a number packages. To attempt to install them all at once, try:
Alfaro, Esteban, Matías Gámez, and Noelia García. 2013.
“adabag: An R Package for Classification with Boosting and Bagging.” Journal of Statistical Software 54 (2): 1–35.
https://doi.org/10.18637/jss.v054.i02.
Alfaro, Esteban; Gamez, Matias, Garcia, Noelia; with contributions from L. Guo, A. Albano, M. Sciandra, and A. Plaia. 2023.
Adabag: Applies Multiclass AdaBoost.M1, SAMME and Bagging.
https://CRAN.R-project.org/package=adabag.
Allaire, JJ, Yihui Xie, Christophe Dervieux, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, et al. 2023.
Rmarkdown: Dynamic Documents for r.
https://CRAN.R-project.org/package=rmarkdown.
Ambroise, Christophe, and Geoffrey J. McLachlan. 2002.
“Selection Bias in Gene Extraction on the Basis of Microarray Gene-Expression Data.” Proceedings of the National Academy of Sciences 99 (10): 6562–66.
https://doi.org/10.1073/pnas.102102699.
Arel-Bundock, Vincent. 2023.
Countrycode: Convert Country Names and Country Codes.
https://vincentarelbundock.github.io/countrycode/.
Arel-Bundock, Vincent, Nils Enevoldsen, and CJ Yetman. 2018.
“Countrycode: An r Package to Convert Country Names and Country Codes.” Journal of Open Source Software 3 (28): 848.
https://doi.org/10.21105/joss.00848.
Boente, Graciela, Alejandra Martínez, and Matías Salibián-Barrera. 2017.
“Robust Estimators for Additive Models Using Backfitting.” Journal of Nonparametric Statistics 29 (4): 744–67.
https://doi.org/10.1080/10485252.2017.1369077.
Breiman, Leo, Adele Cutler, Andy Liaw, and Matthew Wiener. 2022.
randomForest: Breiman and Cutler’s Random Forests for Classification and Regression.
https://www.stat.berkeley.edu/~breiman/RandomForests/.
Csárdi, Gábor, Kirill Müller, and Jim Hester. 2022.
Desc: Manipulate DESCRIPTION Files.
https://CRAN.R-project.org/package=desc.
Dolnicar, Sara, Bettina Gruen, and Friedrich Leisch. 2018.
Market Segmentation Analysis — Understanding It, Doing It, and Making It Useful. Singapore: Springer.
https://doi.org/10.1007/978-981-10-8818-6.
Efron, Bradley. 1986. “How Biased Is the Apparent Error Rate of a Prediction Rule?” Journal of the American Statistical Association 81 (394): 461–70.
Fraley, Chris, Adrian E. Raftery, and Luca Scrucca. 2022.
Mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation.
https://mclust-org.github.io/mclust/.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2000.
“Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors).” The Annals of Statistics 28 (2): 337–407.
https://doi.org/10.1214/aos/1016218223.
Friedman, Jerome, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Kenneth Tay, Noah Simon, and James Yang. 2023.
Glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models.
https://glmnet.stanford.edu.
Friedman, Jerome, Robert Tibshirani, and Trevor Hastie. 2010.
“Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software 33 (1): 1–22.
https://doi.org/10.18637/jss.v033.i01.
Genz, Alan, and Frank Bretz. 2009. Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics. Heidelberg: Springer-Verlag.
Genz, Alan, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, and Torsten Hothorn. 2023.
Mvtnorm: Multivariate Normal and t Distributions.
http://mvtnorm.R-forge.R-project.org.
Halvorsen, Kjetil. 2019.
ElemStatLearn: Data Sets, Functions and Examples from the Book: "The Elements of Statistical Learning, Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman.
http://www-stat.stanford.edu/~tibs/ElemStatLearn/.
Hastie, Trevor, and Brad Efron. 2022.
Lars: Least Angle Regression, Lasso and Forward Stagewise.
https://doi.org/10.1214/009053604000000067.
James, Gareth, Daniela Witten, Trevor Hastie, and Rob Tibshirani. 2022.
ISLR2: Introduction to Statistical Learning, Second Edition.
https://www.statlearning.com.
Kassambara, Alboukadel. 2022.
Ggcorrplot: Visualization of a Correlation Matrix Using Ggplot2.
http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2.
Leisch, Friedrich. 2006. “A Toolbox for k-Centroids Cluster Analysis.” Computational Statistics and Data Analysis 51 (2): 526–44.
———. 2010.
“Neighborhood Graphs, Stripes and Shadow Plots for Cluster Visualization.” Statistics and Computing 20: 457–69.
https://doi.org/10.1007/s11222-009-9137-8.
———. 2022.
Flexclust: Flexible Cluster Algorithms.
https://CRAN.R-project.org/package=flexclust.
Leisch, Friedrich, and Bettina Gruen. 2006. “Extending Standard Cluster Algorithms to Allow for Group Constraints.” In Compstat 2006—Proceedings in Computational Statistics, edited by Alfredo Rizzi and Maurizio Vichi, 885–92. Physica Verlag, Heidelberg, Germany.
Liaw, Andy, and Matthew Wiener. 2002.
“Classification and Regression by randomForest.” R News 2 (3): 18–22.
https://CRAN.R-project.org/doc/Rnews/.
Lumley, Thomas. 2020.
Leaps: Regression Subset Selection.
https://CRAN.R-project.org/package=leaps.
Maechler, Martin, Peter Rousseeuw, Anja Struyf, and Mia Hubert. 2022.
Cluster: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw Et Al. https://svn.r-project.org/R-packages/trunk/cluster/.
Müller, Kirill, and Hadley Wickham. 2023.
Tibble: Simple Data Frames.
https://CRAN.R-project.org/package=tibble.
Ripley, Brian. 2023a.
Class: Functions for Classification.
http://www.stats.ox.ac.uk/pub/MASS4/.
———. 2023b.
MASS: Support Functions and Datasets for Venables and Ripley’s MASS.
http://www.stats.ox.ac.uk/pub/MASS4/.
———. 2023c.
Nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models.
http://www.stats.ox.ac.uk/pub/MASS4/.
———. 2023d.
Tree: Classification and Regression Trees.
https://CRAN.R-project.org/package=tree.
Scharl, Theresa, and Friedrich Leisch. 2006. “The Stochastic QT–Clust Algorithm: Evaluation of Stability and Variance on Time–Course Microarray Data.” In Compstat 2006—Proceedings in Computational Statistics, edited by Alfredo Rizzi and Maurizio Vichi, 1015–22. Physica Verlag, Heidelberg, Germany.
Scrucca, Luca, Michael Fop, T. Brendan Murphy, and Adrian E. Raftery. 2016.
“mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models.” The R Journal 8 (1): 289–317.
https://doi.org/10.32614/RJ-2016-021.
Sievert, Carson, Joe Cheng, and Garrick Aden-Buie. 2023.
Bslib: Custom Bootstrap ’Sass’ Themes for Shiny and Rmarkdown.
https://CRAN.R-project.org/package=bslib.
Simon, Noah, Jerome Friedman, Robert Tibshirani, and Trevor Hastie. 2011.
“Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent.” Journal of Statistical Software 39 (5): 1–13.
https://doi.org/10.18637/jss.v039.i05.
South, Andy. 2011.
“Rworldmap: A New r Package for Mapping Global Data.” The R Journal 3 (1): 35–43.
http://journal.r-project.org/archive/2011-1/RJournal_2011-1_South.pdf.
———. 2016.
Rworldmap: Mapping Global Data.
https://CRAN.R-project.org/package=rworldmap.
Tay, J. Kenneth, Balasubramanian Narasimhan, and Trevor Hastie. 2023.
“Elastic Net Regularization Paths for All Generalized Linear Models.” Journal of Statistical Software 106 (1): 1–31.
https://doi.org/10.18637/jss.v106.i01.
Tharmaratnam, Kukatharmini, Gerda Claeskens, Christophe Croux, and Matias Salibián-Barrera. 2010.
“S-Estimation for Penalized Regression Splines.” Journal of Computational and Graphical Statistics 19 (3): 609–25.
https://doi.org/10.1198/jcgs.2010.08149.
Therneau, Terry, and Beth Atkinson. 2022.
Rpart: Recursive Partitioning and Regression Trees.
https://CRAN.R-project.org/package=rpart.
Todorov, Valentin, and Peter Filzmoser. 2009.
“An Object-Oriented Framework for Robust Multivariate Analysis.” Journal of Statistical Software 32 (3): 1–47.
https://www.jstatsoft.org/article/view/v032i03/.
Todorov, Valentin, Andreas Ruckstuhl, Matias Salibian-Barrera, Tobias Verbeke, Manuel Koller, and Martin Maechler. 2023.
Robustbase: Basic Robust Statistics.
https://robustbase.R-forge.R-project.org/.
Venables, W. N., and B. D. Ripley. 2002b.
Modern Applied Statistics with s. Fourth. New York: Springer.
https://www.stats.ox.ac.uk/pub/MASS4/.
———. 2002a.
Modern Applied Statistics with s. Fourth. New York: Springer.
https://www.stats.ox.ac.uk/pub/MASS4/.
———. 2002c.
Modern Applied Statistics with s. Fourth. New York: Springer.
https://www.stats.ox.ac.uk/pub/MASS4/.
Voeten, Erik, Anton Strezhnev, and Michael Bailey. 2009.
“United Nations General Assembly Voting Data.” Harvard Dataverse.
https://doi.org/10.7910/DVN/LEJUQZ.
Wand, Matt. 2018.
SemiPar: Semiparametic Regression.
http://matt-wand.utsacademics.info/SPmanu.pdf.
———. 2023.
KernSmooth: Functions for Kernel Smoothing Supporting Wand & Jones (1995).
https://CRAN.R-project.org/package=KernSmooth.
Wickham, Hadley. 2016.
Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
https://ggplot2.tidyverse.org.
———. 2023a.
Downlit: Syntax Highlighting and Automatic Linking.
https://CRAN.R-project.org/package=downlit.
———. 2023b.
Tidyverse: Easily Install and Load the Tidyverse.
https://CRAN.R-project.org/package=tidyverse.
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.
Wickham, Hadley, Winston Chang, Robert Flight, Kirill Müller, and Jim Hester. 2021.
Sessioninfo: R Session Information.
https://CRAN.R-project.org/package=sessioninfo.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2023.
Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics.
https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, and Dana Seidel. 2022.
Scales: Scale Functions for Visualization.
https://CRAN.R-project.org/package=scales.
Wood, Simon N. 2017. Generalized Additive Models: An Introduction with r. CRC press.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC.
———. 2015.
Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC.
https://yihui.org/knitr/.
———. 2016.
Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC.
https://bookdown.org/yihui/bookdown.
———. 2023a.
Bookdown: Authoring Books and Technical Documents with r Markdown.
https://CRAN.R-project.org/package=bookdown.
———. 2023c.
Knitr: A General-Purpose Package for Dynamic Report Generation in r.
https://yihui.org/knitr/.
Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018.
R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC.
https://bookdown.org/yihui/rmarkdown.
Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020.
R Markdown Cookbook. Boca Raton, Florida: Chapman; Hall/CRC.
https://bookdown.org/yihui/rmarkdown-cookbook.
Zou, Hui. 2006. “The Adaptive Lasso and Its Oracle Properties.” Journal of the American Statistical Association 101 (476): 1418–29.
Zou, Hui, and Trevor Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2): 301–20.