Acknowledgements

Michael Weylandt

Department of Statistics, Rice University

John Nagorski

Department of Statistics, Rice University

Genevera I. Allen

Departments of Statistics, Computer Science, and Electical and Computer Engineering, Rice University
Jan and Dan Duncan Neurological Research Institute, Baylor College of Medicine

Last Updated: August 19th, 2020

Source: vignettes/Acknowledgements.Rmd

The computational core of clustRviz is written in standard modern C++ (ISO: International Standards Organization 2011), using the Eigen3 library for numerical linear algebra (Guennebaud, Jacob, and others 2010) and the Rcpp (Eddelbuettel and François 2011) and RcppEigen (Bates and Eddelbuettel 2013) libraries to interface with R (R Core Team 2018).

The graphical components of clustRviz depend on several publicly available R packages, including:

  • the dendextend package (Galili 2015) which is used for dendrogram construction and manipulation;
  • the ggplot2 (Wickham 2016) and gganimate (Pedersen and Robinson 2019) packages which are used for path visualizations;
  • the plotly (Sievert 2020) package which is used for interactive dendrograms and path plots; and
  • the heatmaply (Galili et al. 2018) package which is used for export of interactive cluster heatmaps based on CARP and CBASS results

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

References

Bates, Douglas, and Dirk Eddelbuettel. 2013. “Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package.” Journal of Statistical Software 52 (5): 1–24. https://doi.org/10.18637/jss.v052.i05.

Eddelbuettel, Dirk, and Romain François. 2011. “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software 40 (8): 1–18. https://doi.org/10.18637/jss.v040.i08.

Galili, Tal. 2015. “dendextend: An R Package for Visualizing, Adjusting, and Comparing Trees of Hierarchical Clustering.” Bioinformatics 31 (22): 3718–20. https://doi.org/10.1093/bioinformatics/btv428.

Galili, Tal, Alan O’Callaghan, Jonathan Sidi, and Carson Sievert. 2018. “heatmaply: An R Package for Creating Interactive Cluster Heatmaps for Online Publishing.” Bioinformatics 34 (9): 1600–1602. https://doi.org/10.1093/bioinformatics/btx657.

Guennebaud, Gaël, Benoît Jacob, and others. 2010. “Eigen V3.” http://eigen.tuxfamily.org.

ISO: International Standards Organization. 2011. ISO/Iec 14882:2011. Information Technology – Programming Languages – C++. ISO. https://www.iso.org/standard/50372.html.

Pedersen, Thomas Lin, and David Robinson. 2019. gganimate: A Grammar of Animated Graphics. https://cran.r-project.org/package=gganimate.

R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Sievert, Carson. 2020. Interactive Web-Based Data Visualization with R, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.

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