References

Ahamada, Ibrahim, and Emmanuel Flachaire. 2011. Non-Parametric Econometrics. Oxford University Press.
Allaire, JJ, and François Chollet. 2022. Keras: R Interface to Keras. https://tensorflow.rstudio.com/.
Allaire, JJ, and Yuan Tang. 2022. Tensorflow: R Interface to TensorFlow. https://github.com/rstudio/tensorflow.
Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2023. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.
Alpaydin, Ethem. 2014. Introduction to Machine Learning. 3rd ed. Cambridge, MA: MIT Press.
Arnholt, Alan T. 2022. PASWR: Probability and Statistics with r. https://CRAN.R-project.org/package=PASWR.
Atkinson, Elizabeth J., and Terry M. Therneau. 2022. “An Introduction to Recursive Partitioning Using the RPART Routines.” https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf.
Atkinson, Elizabeth J., Terry M. Therneau, and Mayo Foundation. 2000. “An Introduction to Recursive Partitioning Using the RPART Routines.” https://www.mayo.edu/research/documents/rpartminipdf/doc-10027257.
Baumer, Matthew. 2015. “K Nearest Neighbors.” https://rpubs.com/mbaumer/knn.
Beck, Marcus W. 2018. NeuralNetTools: Visualization and Analysis Tools for Neural Networks.” Journal of Statistical Software 85 (11): 1–20. https://doi.org/10.18637/jss.v085.i11.
———. 2022. NeuralNetTools: Visualization and Analysis Tools for Neural Networks. https://CRAN.R-project.org/package=NeuralNetTools.
Bergstra, James, and Yoshua Bengio. 2012b. “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research 13: 281–305. https://jmlr.csail.mit.edu/papers/volume13/bergstra12a/bergstra12a.pdf .
———. 2012a. “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research 13: 281–305. https://jmlr.csail.mit.edu/papers/volume13/bergstra12a/bergstra12a.pdf .
Bernaards, Coen A., and Robert I.Jennrich. 2005. “Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis.” Educational and Psychological Measurement 65: 676–96.
Bernaards, Coen, Paul Gilbert, and Robert Jennrich. 2022. GPArotation: GPA Factor Rotation. https://optimizer.r-forge.r-project.org/GPArotation_www/.
Biecek, Przemyslaw. 2018. “DALEX: Explainers for Complex Predictive Models in r.” Journal of Machine Learning Research 19 (84): 1–5. https://jmlr.org/papers/v19/18-416.html.
Biecek, Przemyslaw, and Tomasz Burzykowski. 2021. Explanatory Model Analysis. Chapman; Hall/CRC, New York. https://pbiecek.github.io/ema/.
Biecek, Przemyslaw, Szymon Maksymiuk, and Hubert Baniecki. 2022. DALEX: moDel Agnostic Language for Exploration and eXplanation. https://CRAN.R-project.org/package=DALEX.
Brabec, Jan, and Lukás Machlica. 2018. “Bad Practices in Evaluation Methodology Relevant to Class-Imbalanced Problems.” CoRR abs/1812.01388. https://arxiv.org/pdf/1812.01388.pdf.
Breiman, Leo. 2001. “Statistical Modeling: The Two Cultures.” Statistical Science 16 (3): 199–231.
Breiman, Leo, and Adele Cutler. 2004. “Random Forests.” https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm .
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/.
Brownlee, Jason. 2017. “What Is the Difference Between Test and Validation Datasets?” https://machinelearningmastery.com/difference-test-validation-datasets/.
Chakraborty, Supriyo, Richard Tomsett, Ramya Raghavendra, Daniel Harborne, Moustafa Alzantot, Federico Cerutti, Mani Srivastava, et al. 2017. “Interpretability of Deep Learning Models: A Survey of Results.” In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 1–6. https://doi.org/10.1109/UIC-ATC.2017.8397411.
Charpentier, Arthur. 2016. “Regression with Splines: Should We Care about Non-Significant Components?” Freakonometrics. https://freakonometrics.hypotheses.org/47681.
———. 2018a. “Classification from Scratch, Boosting 11/8.” Freakonometrics. https://freakonometrics.hypotheses.org/52782.
———. 2018b. “Classification from Scratch, Trees 9/8.” Freakonometrics. https://freakonometrics.hypotheses.org/52776.
Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-Sampling Technique.” Journal of Artificial Intelligence Research 16: 321–57. https://jair.org/index.php/jair/article/view/10302.
Chen, Tianqi, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, et al. 2023. Xgboost: Extreme Gradient Boosting. https://github.com/dmlc/xgboost.
Cho, KyungHyun, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. “On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.” CoRR abs/1409.1259. http://arxiv.org/abs/1409.1259.
Corporation, Microsoft, and Steve Weston. 2022. doParallel: Foreach Parallel Adaptor for the Parallel Package. https://github.com/RevolutionAnalytics/doparallel.
Cortez, Paulo. 2020. Rminer: Data Mining Classification and Regression Methods. https://cran.r-project.org/package=rminer http://www3.dsi.uminho.pt/pcortez/rminer.html.
Cortez, Paulo, António Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. 2009. “Modeling Wine Preferences by Data Mining from Physicochemical Properties.” Decision Support Systems 47 (4): 547–53. https://doi.org/https://doi.org/10.1016/j.dss.2009.05.016.
Croissant, Yves, and Giovanni Millo. 2008. “Panel Data Econometrics in R: The plm Package.” Journal of Statistical Software 27 (2): 1–43. https://doi.org/10.18637/jss.v027.i02.
———. 2018. Panel Data Econometrics with R. Wiley.
Croissant, Yves, Giovanni Millo, and Kevin Tappe. 2022. Plm: Linear Models for Panel Data. https://CRAN.R-project.org/package=plm.
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323: 533–36. https://doi.org/10.1038/323533a0.
DeBruine, Lisa. 2021. Faux: Simulation for Factorial Designs. https://github.com/debruine/faux.
Dickenson-Jones, Giles. 2019. “7 Reasons for Policy Professionals to Get into r Programming in 2019.” http://gilesd-j.com/2019/01/07/7-reasons-for-policy-professionals-to-get-pumped-about-r-programming-in-2019/.
Dowle, Matt, and Arun Srinivasan. 2022. Data.table: Extension of ‘Data.frame‘. https://CRAN.R-project.org/package=data.table.
FRED. 2015. “FRED.” Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/.
Freund, Yoav, and Robert E. Schapire. 1997. “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting.” Journal of Computer and System Sciences 55 (1): 119–39. https://doi.org/https://doi.org/10.1006/jcss.1997.1504.
Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. 2019. Glasso: Graphical Lasso: Estimation of Gaussian Graphical Models. http://www-stat.stanford.edu/~tibs/glasso.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 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.
Friedman, Jerome, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Kenneth Tay, Noah Simon, and James Yang. 2022. Glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. https://CRAN.R-project.org/package=glmnet.
Fritsch, Stefan, Frauke Guenther, and Marvin N. Wright. 2019. Neuralnet: Training of Neural Networks. https://github.com/bips-hb/neuralnet.
Gilbert, Paul, and Ravi Varadhan. 2019. numDeriv: Accurate Numerical Derivatives. http://optimizer.r-forge.r-project.org/.
Gorman, Ben. 2018. Mltools: Machine Learning Tools. https://github.com/ben519/mltools.
Greenwell, Brandon M., and Bradley C. Boehmke. 2020. “Variable Importance Plots—an Introduction to the Vip Package.” The R Journal 12 (1): 343–66. https://doi.org/10.32614/RJ-2020-013.
Greenwell, Brandon, Brad Boehmke, and Bernie Gray. 2020. Vip: Variable Importance Plots. https://github.com/koalaverse/vip/.
Greenwell, Brandon, Bradley Boehmke, Jay Cunningham, and GBM Developers. 2022. Gbm: Generalized Boosted Regression Models. https://github.com/gbm-developers/gbm.
Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. https://www.jstatsoft.org/v40/i03/.
Guerrero, Victor M. 1993. “Time-Series Analysis Supported by Power Transformations.” Journal of Forecasting 12 (1): 37–48. https://doi.org/https://doi.org/10.1002/for.3980120104.
Gulzar. 2018. Cross Validated. https://stats.stackexchange.com/q/376191.
Hastie, Trevor, Junyang Qian, and Kenneth Tay. 2021. “An Introduction to Glmnet.” https://glmnet.stanford.edu/articles/glmnet.html.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer. https://hastie.su.domains/ElemStatLearn/.
Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. 1st ed. Chapman; Hall/CRC. https://hastie.su.domains/StatLearnSparsity/.
Helwig, Nathaniel E. 2022. Npreg: Nonparametric Regression via Smoothing Splines. https://CRAN.R-project.org/package=npreg.
Hlavac, Marek. 2022. Stargazer: Well-Formatted Regression and Summary Statistics Tables. https://CRAN.R-project.org/package=stargazer.
Hyndman, Rob. 2021. Fpp3: Data for "Forecasting: Principles and Practice" (3rd Edition). https://CRAN.R-project.org/package=fpp3.
Hyndman, Rob J, and George Athanasopoulos. 2021. Forecasting: Principles and Practice. 3rd ed. OTexts: Melbourne, Australia. https://otexts.com/fpp3/.
Hyndman, Rob J., and Yeasmin Khandakar. 2008a. “Automatic Time Series Forecasting: The Forecast Package for r.” Journal of Statistical Software 27 (3): 1–22. https://doi.org/10.18637/jss.v027.i03.
Hyndman, Rob J, and Yeasmin Khandakar. 2008b. “Automatic Time Series Forecasting: The Forecast Package for R.” Journal of Statistical Software 26 (3): 1–22. https://doi.org/10.18637/jss.v027.i03.
Hyndman, Rob, George Athanasopoulos, Christoph Bergmeir, Gabriel Caceres, Leanne Chhay, Kirill Kuroptev, Mitchell O’Hara-Wild, et al. 2022. Forecast: Forecasting Functions for Time Series and Linear Models. https://CRAN.R-project.org/package=forecast.
Irizarry, Rafael A. 2022. Data Analysis and Prediction Algorithms with r. Bookdown. https://rafalab.github.io/dsbook/.
Irizarry, Rafael A., and Amy Gill. 2021. Dslabs: Data Science Labs. https://CRAN.R-project.org/package=dslabs.
ISLR. 2021. “Carseats: Sales of Child Car Seats.” ISLR. https://rdrr.io/cran/ISLR/man/Carseats.html .
James, Gareth, Daniela Witten, Trevor Hastie, and Rob Tibshirani. 2021. ISLR: Data for an Introduction to Statistical Learning with Applications in r. https://www.statlearning.com.
———. 2022. ISLR2: Introduction to Statistical Learning, Second Edition. https://www.statlearning.com.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. 1st ed. Springer New York, NY. https://doi.org/10.1007/978-1-4614-7138-7.
Kassambara, Alboukadel, and Fabian Mundt. 2020. Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. http://www.sthda.com/english/rpkgs/factoextra.
Kim, Seongho. 2015. Ppcor: Partial and Semi-Partial (Part) Correlation. https://CRAN.R-project.org/package=ppcor.
Kleiber, Christian, and Achim Zeileis. 2008. Applied Econometrics with R. New York: Springer-Verlag. https://CRAN.R-project.org/package=AER.
———. 2022. AER: Applied Econometrics with r. https://CRAN.R-project.org/package=AER.
Kohavi, Ronny, and Barry Becker. 1996. “Adult Data Set.” University of California, Irvine, School of Information & Computer Sciences. https://archive.ics.uci.edu/ml/datasets/Adult.
Kuhn, Max. 2019. The Caret Package. Bookdown. https://topepo.github.io/caret/index.html.
———. 2020. AmesHousing: The Ames Iowa Housing Data. https://github.com/topepo/AmesHousing.
———. 2022a. Caret: Classification and Regression Training. https://github.com/topepo/caret/.
———. 2022b. Modeldata: Data Sets Useful for Modeling Examples. https://CRAN.R-project.org/package=modeldata.
Larsson, Johan. 2022. Eulerr: Area-Proportional Euler and Venn Diagrams with Ellipses. https://CRAN.R-project.org/package=eulerr.
Larsson, Johan, and Peter Gustafsson. 2018. “A Case Study in Fitting Area-Proportional Euler Diagrams with Ellipses Using Eulerr.” In Proceedings of International Workshop on Set Visualization and Reasoning, 2116:84–91. Edinburgh, United Kingdom: CEUR Workshop Proceedings. https://cran.r-project.org/package=eulerr.
Leathwick, J. R., J. Elith, and T. Hastie. 2006. “Comparative Performance of Generalized Additive Models and Multivariate Adaptive Regression Splines for Statistical Modelling of Species Distributions.” Ecological Modelling 199 (2): 188–96. https://www.sciencedirect.com/science/article/pii/S0304380006002572.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–44. https://doi.org/10.1038/nature14539.
Lecun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–2324. https://doi.org/10.1109/5.726791.
Liaw, Andy, and Matthew Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22. https://CRAN.R-project.org/doc/Rnews/.
Mahdi, Salsabila, Akshaj Verma, Christophe Dutang, Patrice Kiener, and John C. Nash. 2022. A 2019-2020 Review of R Neural Network Packages with NNbenchmark.” Zenodo. https://doi.org/10.5281/zenodo.7415417.
McCulloch, Warren S., and Walter Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Bulletin of Mathematical Biophysics 5: 115–33. https://doi.org/10.1007/BF02478259.
Meyer, David, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel, and Friedrich Leisch. 2022. E1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. https://CRAN.R-project.org/package=e1071.
Milborrow, Stephen. 2022. Rpart.plot: Plot Rpart Models: An Enhanced Version of Plot.rpart. http://www.milbo.org/rpart-plot/index.html.
Millo, Giovanni. 2017. “Robust Standard Error Estimators for Panel Models: A Unifying Approach.” Journal of Statistical Software 82 (3): 1–27. https://doi.org/10.18637/jss.v082.i03.
Molnar, Christoph. 2021. Interpretable Machine Learning. https://christophm.github.io/interpretable-ml-book/.
Müller, Kirill, and Hadley Wickham. 2022. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
O’Hara-Wild, Mitchell, Rob Hyndman, and Earo Wang. 2022. Fable: Forecasting Models for Tidy Time Series. https://CRAN.R-project.org/package=fable.
Olson, Matthew. 2017. JOUSBoost: Implements Under/Oversampling for Probability Estimation. https://CRAN.R-project.org/package=JOUSBoost.
Paluszynska, Aleksandra, Przemyslaw Biecek, and Yue Jiang. 2020. randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance. https://github.com/ModelOriented/randomForestExplainer.
Paluszyńska, Aleksandra. 2017. “Understanding Random Forests with randomForestExplainer.” https://htmlpreview.github.io/?https://github.com/geneticsMiNIng/BlackBoxOpener/master/randomForestExplainer/inst/doc/randomForestExplainer.html.
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why. 1st ed. Basic Books.
Peeters, Carel F. W., Anders Ellern Bilgrau, and Wessel N. van Wieringen. 2022a. rags2ridges: A One-Stop-\(\ell_2\)-Shop for Graphical Modeling of High-Dimensional Precision Matrices.” Journal of Statistical Software 102 (4): 1–32. https://doi.org/10.18637/jss.v102.i04.
———. 2022b. Rags2ridges: Ridge Estimation of Precision Matrices from High-Dimensional Data. https://CRAN.R-project.org/package=rags2ridges.
Pfann, Gerard A., Peter C. Schotman, and Rolf Tschernig. 1996. “Nonlinear Interest Rate Dynamics and Implications for the Term Structure.” Journal of Econometrics 74 (1): 149–76. https://doi.org/https://doi.org/10.1016/0304-4076(95)01754-2.
Proietti, Tommaso, and Helmut Lütkepohl. 2013. “Does the Box–Cox Transformation Help in Forecasting Macroeconomic Time Series?” International Journal of Forecasting 29 (1): 88–99. https://doi.org/https://doi.org/10.1016/j.ijforecast.2012.06.001.
R Core Team. 2022a. Foreign: Read Data Stored by Minitab, s, SAS, SPSS, Stata, Systat, Weka, dBase, ... https://svn.r-project.org/R-packages/trunk/foreign/.
———. 2022b. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Rajter, M. 2019. “In Memory of Monty Hall.” https://theressomethingaboutr.wordpress.com/2019/02/12/in-memory-of-monty-hall/.
Revelle, William. 2022. Psych: Procedures for Psychological, Psychometric, and Personality Research. https://personality-project.org/r/psych/ https://personality-project.org/r/psych-manual.pdf.
Revolution Analytics, and Steve Weston. n.d. Foreach: Provides Foreach Looping Construct.
Ridgeway, Greg. 2020. “Generalized Boosted Models: A Guide to the Gbm Package.” https://cran.r-project.org/web/packages/gbm/vignettes/gbm.pdf .
Ripley, Brian. 2022. MASS: Support Functions and Datasets for Venables and Ripley’s MASS. http://www.stats.ox.ac.uk/pub/MASS4/.
Rivolli, Adriano. 2021. Utiml: Utilities for Multi-Label Learning. https://github.com/rivolli/utiml.
Robinson, David, Alex Hayes, and Simon Couch. 2022. Broom: Convert Statistical Objects into Tidy Tibbles. https://CRAN.R-project.org/package=broom.
Rosenblatt, F. 1958. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review 65 (6): 386–408. https://doi.org/10.1038/323533a0.
Schafer, Juliane, Rainer Opgen-Rhein, Verena Zuber, Miika Ahdesmaki, A. Pedro Duarte Silva, and Korbinian Strimmer. 2021. Corpcor: Efficient Estimation of Covariance and (Partial) Correlation. https://strimmerlab.github.io/software/corpcor/.
Schapire, Robert E. 1990. “The Strength of Weak Learnability.” Machine Learning 5: 197–227. https://web.archive.org/web/20121010030839/http://www.cs.princeton.edu/~schapire/papers/strengthofweak.pdf.
Schloerke, Barret, Di Cook, Joseph Larmarange, Francois Briatte, Moritz Marbach, Edwin Thoen, Amos Elberg, and Jason Crowley. 2021. GGally: Extension to Ggplot2. https://CRAN.R-project.org/package=GGally.
Simon, Noah, Jerome Friedman, Trevor Hastie, and Rob Tibshirani. 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.
Sing, Tobias, Oliver Sander, Niko Beerenwinkel, and Thomas Lengauer. 2020. ROCR: Visualizing the Performance of Scoring Classifiers. http://ipa-tys.github.io/ROCR/.
Sing, T., O. Sander, N. Beerenwinkel, and T. Lengauer. 2005. “ROCR: Visualizing Classifier Performance in r.” Bioinformatics 21 (20): 7881. http://rocr.bioinf.mpi-sb.mpg.de.
Siriseriwan, Wacharasak. 2019. Smotefamily: A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE. https://CRAN.R-project.org/package=smotefamily.
Spinu, Vitalie, Garrett Grolemund, and Hadley Wickham. 2022. Lubridate: Make Dealing with Dates a Little Easier. https://CRAN.R-project.org/package=lubridate.
Stock, James, and Mark Watson. 2015. Introduction to Econometrics (3rd Edition). Addison Wesley Longman; Addison Wesley Longman.
Sustik, Matyas A, Ben Calderhead, and Julien Clavel. 2018. glassoFast: Fast Graphical LASSO. https://CRAN.R-project.org/package=glassoFast.
Taieb, Souhaib Ben, and Rob J Hyndman. 2012. Recursive and direct multi-step forecasting: the best of both worlds.” Monash University. https://robjhyndman.com/papers/rectify.pdf.
Temple Lang, Duncan. 2022. RCurl: General Network (HTTP/FTP/...) Client Interface for r. https://CRAN.R-project.org/package=RCurl.
Therneau, Terry, and Beth Atkinson. 2022. Rpart: Recursive Partitioning and Regression Trees. https://CRAN.R-project.org/package=rpart.
Trevor Hastie, Stephen Milborrow. Derived from mda:mars by, and Rob Tibshirani. Uses Alan Miller’s Fortran utilities with Thomas Lumley’s leaps wrapper. 2021. Earth: Multivariate Adaptive Regression Splines. http://www.milbo.users.sonic.net/earth/.
Turner, Rolf. 2021. Deldir: Delaunay Triangulation and Dirichlet (Voronoi) Tessellation. https://CRAN.R-project.org/package=deldir.
UCLA. 2021. “R Library Introduction to Bootstrapping.” ULCA Advanced Research Computing Statistical Methods & Data Analytics. https://stats.oarc.ucla.edu/r/library/r-library-introduction-to-bootstrapping/.
van Wieringen, Wessel N., and Carel F. W. Peeters. 2016. “Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data.” Computational Statistics & Data Analysis 103: 284–303. https://doi.org/https://doi.org/10.1016/j.csda.2016.05.012.
Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with s. Fourth. New York: Springer. https://www.stats.ox.ac.uk/pub/MASS4/.
Wang, Earo, Dianne Cook, and Rob J Hyndman. 2020a. “A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data.” Journal of Computational and Graphical Statistics 29 (3): 466–78. https://doi.org/10.1080/10618600.2019.1695624.
Wang, Earo, Dianne Cook, and Rob J. Hyndman. 2020b. “A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data.” Journal of Computational and Graphical Statistics 29 (3): 466–78. https://doi.org/10.1080/10618600.2019.1695624.
Wang, Earo, Di Cook, Rob Hyndman, and Mitchell O’Hara-Wild. 2022. Tsibble: Tidy Temporal Data Frames and Tools. https://tsibble.tidyverts.org.
Wei, Taiyun, and Viliam Simko. 2021a. Corrplot: Visualization of a Correlation Matrix. https://github.com/taiyun/corrplot.
———. 2021b. R Package ’Corrplot’: Visualization of a Correlation Matrix. https://github.com/taiyun/corrplot.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
———. 2020. Fueleconomy: EPA Fuel Economy Data. https://github.com/hadley/fueleconomy.
———. 2022. 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, 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, Romain François, Lionel Henry, and Kirill Müller. 2022. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, Jim Hester, and Jennifer Bryan. 2022. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.
Williams, Graham. 2022. Rattle: Graphical User Interface for Data Science in r. https://rattle.togaware.com/.
Williams, Graham J. 2011. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Use r! Springer. https://rd.springer.com/book/10.1007/978-1-4419-9890-3.
Wood, S. N. 2017. Generalized Additive Models: An Introduction with r. 2nd ed. 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, and B. S"afken. 2016. “Smoothing Parameter and Model Selection for General Smooth Models (with Discussion).” Journal of the American Statistical Association 111: 1548–75.
Wood, Simon. 2022. Mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. https://CRAN.R-project.org/package=mgcv.
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.
———. 2015b. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.org/knitr/.
———. 2015a. 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.
———. 2023b. 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. https://doi.org/10.1198/016214506000000735.