S3 Object & Method

This part presents technical details of these packages intended for readers with advanced expertise in the R programming language, including minor and/or experimental features, to avoid disrupting the focus and flow of the main narrative in this Quarto book. Readers are expected to have a solid understanding of the S3 object-oriented programming system in R and be familiar with the S3 generic functions and methods from

• R version 4.5.2 (2025-10-31) packages base, graphics, stats, utils, etc.
• the spatstat.* family of packages (Baddeley, Rubak, and Turner 2015; Baddeley and Turner 2005), especially spatstat.geom (v3.6.1), spatstat.explore (v3.6.0.1), spatstat.random (v3.4.3).
• other contributed packages, e.g., mgcv (Wood 2017, v1.9.4), rpart (Therneau and Atkinson 2025, v4.1.24), etc.

This part is organized by the S3 object class, in alphabetical order, then the S3 methods for each class. The names of S3 classes defined in these packages are rendered in italic font, e.g.,

• fvlist in 16  fvlist from anylist
• groupedHyperframe in 19  groupedHyperframe
• hyper_gam in 21  hyper_gam from gam
• node1 in 24  node1
• vectorlist in 32  vectorlist from anylist

37  Statistical Topics, on the other hand, organizes the content of this part according to the statistical functionalities, i.e., the S3 generic functions. 37  Statistical Topics is an ideal starting point for readers who are not experts in R.

The R code-chunks in this part are categorized by purpose, including,

• Data, to create and/or inspect R objects for subsequent operations.
• Review, to demonstrate usage of functions from R version 4.5.2 (2025-10-31), or from contributed packages other than these packages.
• Example, to show how to use functions from these packages.
• Exception, to demonstrate the exception-handling mechanisms of functions from these packages.
• Previously, to copy R code from earlier sections for the reader’s convenience.
• Advanced, to discuss advanced topics and experimental features for expert R users.
• Figure, to generate elegant visualizations.
• Workaround, to present alternative solutions to specific problems.
• Table, to create HTML tables using function knitr::kable() (Xie 2025).
• Future, to list upcoming tasks for these packages.

Readers aiming to extend the these packages or integrate them into their own workflows are strongly encouraged to study the Appendix thoroughly.

Baddeley, Adrian, Ege Rubak, and Rolf Turner. 2015. Spatial Point Patterns: Methodology and Applications with R. London: Chapman; Hall/CRC Press. https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/p/book/9781482210200/.

Baddeley, Adrian, and Rolf Turner. 2005. “spatstat: An R Package for Analyzing Spatial Point Patterns.” Journal of Statistical Software 12 (6): 1–42. https://doi.org/10.18637/jss.v012.i06.

Therneau, Terry, and Beth Atkinson. 2025. rpart: Recursive Partitioning and Regression Trees. https://CRAN.R-project.org/package=rpart.

Wood, Simon N. 2017. Generalized Additive Models: An Introduction with R. 2nd ed. Chapman; Hall/CRC.

Xie, Yihui. 2025. knitr: A General-Purpose Package for Dynamic Report Generation in R. https://yihui.org/knitr/.