Acknowledgements

Royal Bank of Canada
vathy.kamulete@rbc.com

The key components of dsos depend on several publicly available R packages, including:

• the data.table package (Dowle and Srinivasan 2020) for faster data.frame.
• the ranger package (Wright and Ziegler 2017) for random forest;
• the isotree package (Cortes 2020) for isolation-based outlier detection;
• the ggplot2 package (Wickham 2016) for visualizations;
• the WeightedROC package (Hocking 2020) for computing the test statistic;
• the simctest package (Gandy 2009) for sequential Monte Carlo tests;

We are grateful to the authors of each of these packages for making their software freely available.

References

Cortes, David. 2020. Isotree: Isolation-Based Outlier Detection. https://CRAN.R-project.org/package=isotree.

Dowle, Matt, and Arun Srinivasan. 2020. Data.table: Extension of ‘Data.frame‘. https://CRAN.R-project.org/package=data.table.

Gandy, Axel. 2009. “Sequential Implementation of Monte Carlo Tests with Uniformly Bounded Resampling Risk.” Journal of the American Statistical Association 104 (488): 1504–11.

Hocking, Toby Dylan. 2020. WeightedROC: Fast, Weighted ROC Curves. https://CRAN.R-project.org/package=WeightedROC.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

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.