Referencias

• Arthur Charpentier (edited). 2015. Computational Actuarial Science with R. Chapman & HAll/CRC. The R Series.
• Christophe Dutang, Vincent Goulet, and Mathieu Pigeon. 2008. “Actuar: An R Package for Actuarial Science.” Journal of Statistical Software 25 (7): 38. https://www.jstatsoft.org/v25/i07.
• Edward W. Frees, Richard A. Derrig, and Glenn Meyers. 2014. Predictive Modeling Applications in Actuarial Science. Volume 1: Predictive Modeling Techniques. Cambridge University Press. International Series on Actuarial Science. https://instruction.bus.wisc.edu/jfrees/jfreesbooks/PredictiveModelingVol1/index.html
• https://openacttexts.github.io/
• Frees, Edward W. 2010. Regression Modeling with Actuarial and Financial Applications. Cambridge University Press. International Series on Actuarial Science. https://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression%20Modeling/BookWebDec2010/home.html.
• https://rpubs.com/.
• Kuhn, Max, and Hadley Wickham. 2020. Tidymodels: A Collection of Packages for Modeling and Machine Learning Using Tidyverse Principles. https://www.tidymodels.org/
• https://www.shinyapps.io/.
• Bharath Ramsundar, Patrick Walters, Peter Eastman, and Vijay Pande. 2019. Deep Learning for the Life Sciences. Applying Deep Learning to Genomics Microscopy, Drug Discovery, and More. O’Reilly Media.
• Gressling, Thorsten. 2021. Data Science in Chemistry. Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter. De Gruyter.
• Haghighatlari, Mojtaba, and Johannes Hachmann. 2019. “Advances of Machine Learning in Molecular Modeling and Simulation.” Current Opinion in Chemical Engineering 23: 51–57. https://www.researchgate.net/publication/330845218_Advances_of_Machine_Learning_in_Molecular_Modeling_and_Simulation.
• Draxl, C.; Scheffler, and M. 2018. “NOMAD: The FAIR Concept for Big Data-Driven Materials Science.” MRS Bulletin 43,: 676–82. https://arxiv.org/abs/1805.05039.
• https://ctr.fandom.com/wiki/Chemistry_Toolkit_Rosetta_Wiki
• https://opsin.ch.cam.ac.uk/
• https://cirpy.readthedocs.io/en/latest/
• https://netflixtechblog.com/
• https://thatdatatho.com/
• Akalin, Altuna. 2021. Computational Genomics with R. Chapman & HAll/CRC. Computational Biology Series. https://compgenomr.github.io/book/
• Bennett, K. P. 1992. “Decision Tree Construction via Linear Programming.” Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, 97–101.
• Timbers, Tiffany-Anne, Trevor Campbell, and Melissa Lee. 2021. Data Science: A First Introduction. Bookdown. https://ubc-dsci.github.io/introduction-to-datascience/.
• Hvitfeldt, Emil. 2021. ISLR Tidymodels Labs. Bookdown. https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html.
• Lucas, Beau. 2020. A Tidy Introduction to Statistical Learning. Github. https://beaulucas.github.io/tidy_islr/.
• https://uc-r.github.io
• http://cox.csueastbay.edu/~esuess/stat654/Poster/
• Allaire, JJ, and François Chollet. 2021. Keras: R Interface to ’Keras’. https://CRAN.R-project.org/package=keras.
• Allaire, JJ, and Yuan Tang. 2021. Tensorflow: R Interface to ’TensorFlow’. https://CRAN.R-project.org/package=tensorflow.
• Arnold, Taylor. 2017. kerasR: R Interface to the Keras Deep Learning Library. https://CRAN.R-project.org/package=kerasR.
• LeDell, Erin, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, et al. 2021. H2o: R Interface for the ’H2o’ Scalable Machine Learning Platform. https://CRAN.R-project.org/package=h2o.
• Newman, D. J., S. Hettich, C. L. Blake, and C. J. Merz. 1998. “UCI Repository of Machine Learning Databases.” University of California, Irvine, Dept. of Information; Computer Sciences. http://www.ics.uci.edu/~mlearn/MLRepository.html.
• Ng, Andrew. 2017. “Machine Learning.” https://www.coursera.org/learn/machine-learning#about.
• https://www.kaggle.com/
• Chollet, Francois, and J. J. Allaire. 2018. Deep Learning with R. Manning Publications. https://livebook.manning.com/book/deep-learning-with-r/table-of-contents/
• Rong, Xiao. 2014. Deepnet: Deep Learning Toolkit in R. https://CRAN.R-project.org/package=deepnet.
• Stefan Fritsch, Frauke Guenther and Marvin N. Wright (2019). neuralnet: Training of Neural Networks. R package version 1.44.2. https://CRAN.R-project.org/package=neuralnet
• Chen, Tianqi, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, et al. 2021. Xgboost: Extreme Gradient Boosting. https://CRAN.R-project.org/package=xgboost.
• 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.
• Kuhn, Max. 2020. Dials: Tools for Creating Tuning Parameter Values. https://CRAN.R-project.org/package=dials.
• ———. 2021. Tune: Tidy Tuning Tools. https://CRAN.R-project.org/package=tune.
• Kuhn, Max, and Davis Vaughan. 2021. Parsnip: A Common API to Modeling and Analysis Functions. https://CRAN.R-project.org/package=parsnip.
• ———. 2021. Recipes: Preprocessing Tools to Create Design Matrices. https://CRAN.R-project.org/package=recipes.
• Leisch, Friedrich, and Evgenia Dimitriadou. 2021. Mlbench: Machine Learning Benchmark Problems.
• Liaw, Andy, and Matthew Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22. https://CRAN.R-project.org/doc/Rnews/.
• Therneau, Terry, and Beth Atkinson. 2019. Rpart: Recursive Partitioning and Regression Trees. https://CRAN.R-project.org/package=rpart.
• Boehmke, Bradley, and Brandon Greenwell. 2020. Hands-on Machine Learning with R. CRC-Press. https://bradleyboehmke.github.io/HOML/.
• Wright, Marvin N., and Andreas Ziegler. 2017. “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software 77 (1): 1–17. https://doi.org/10.18637/jss.v077.i01.
• 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.
• Warnes, Gregory R., Ben Bolker, Thomas Lumley, Randall C Johnson. Contributions from Randall C. Johnson are Copyright SAIC-Frederick, Inc. Funded by the Intramural Research Program, of the NIH, National Cancer Institute, and Center for Cancer Research under NCI Contract NO1-CO-12400. 2018. Gmodels: Various R Programming Tools for Model Fitting. https://CRAN.R-project.org/package=gmodels.
• Davis, Sean, and Paul Meltzer. 2007. “GEOquery: A Bridge Between the Gene Expression Omnibus (GEO) and BioConductor.” Bioinformatics 14: 1846–47.
• Mangiola, Stefano. 2021. tidySummarizedExperiment: Brings SummarizedExperiment to the Tidyverse.
• Mangiola, Stefano, Ramyar Molania, Ruining Dong, and Maria A. Doyle & Anthony T. Papenfuss. 2021. “Tidybulk: An R Tidy Framework for Modular Transcriptomic Data Analysis.” Genome Biology 22 (42). https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02233-7.
• R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
• RStudio Team. 2019. RStudio: Integrated Development Environment for R. Boston, MA: RStudio, Inc. http://www.rstudio.com/.
• Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
• Sievert, Carson. 2020. Interactive Web-Based Data Visualization with R, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.
• https://stemangiola.github.io/bioc2021_tidytranscriptomics/articles/tidytranscriptomics.htmlK
• Kempar, M. 2021. Analyisis of Gene Epression. Bookdown. https://mkempenaar.github.io/gene_expression_analysis/
• Gu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics.
• O’Meara, B. 2020. Comparative Methods. Bookdown. https://bookdown.org/bomeara/comparative-methods/
• O’Meara, B. 2018. Phylogenetics, Especially Comparative Methods. CRAN Task View.
• http://www.randigriffin.com/index.html
• Guangchuang Yu. Using ggtree to visualize data on tree-like structures. Current Protocols in Bioinformatics, 2020, 69:e96. doi: 10.1002/cpbi.96