@article{JSSv050i02, title={Data Analysis with the Morse-Smale Complex: The msr Package for R}, volume={50}, url={https://www.jstatsoft.org/index.php/jss/article/view/v050i02}, doi={10.18637/jss.v050.i02}, abstract={In many areas, scientists deal with increasingly high-dimensional data sets. An important aspect for these scientists is to gain a qualitative understanding of the process or system from which the data is gathered. Often, both input variables and an outcome are observed and the data can be characterized as a sample from a high-dimensional scalar function. This work presents the R package <b>msr</b> for exploratory data analysis of multivariate scalar functions based on the Morse-Smale complex. The Morse-Smale complex provides a topologically meaningful decomposition of the domain. The <b>msr</b> package implements a discrete approximation of the Morse-Smale complex for data sets. In previous work this approximation has been exploited for visualization and partition-based regression, which are both supported in the <b>msr</b> package. The visualization combines the Morse-Smale complex with dimension-reduction techniques for a visual summary representation that serves as a guide for interactive exploration of the high-dimensional function. In a similar fashion, the regression employs a combination of linear models based on the Morse-Smale decomposition of the domain. This regression approach yields topologically accurate estimates and facilitates interpretation of general trends and statistical comparisons between partitions. In this manner, the <b>msr</b> package supports high-dimensional data understanding and exploration through the Morse-Smale complex.}, number={2}, journal={Journal of Statistical Software}, author={Gerber, Samuel and Potter, Kristin}, year={2012}, pages={1–22} }