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 (v3.7.0.6), spatstat.explore (v3.7.0.4), spatstat.random (v3.4.4).
• other contributed packages, e.g., GET (Myllymäki and Mrkvička 2024, v1.0.7), 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.,

• add_dummy_ in 12  add_dummy_ from node1,function
• add_numeric_ in 13  add_numeric_ from call
• cumv in 15  cumv
• fvlist in 20  fvlist from anylist
• groupedHyperframe in 24  groupedHyperframe
• hyper_gam in 26  hyper_gam from gam
• node1 in 31  node1 from function
• vectorlist in 42  vectorlist from anylist

57  Statistical Topics, on the other hand, organizes the content of this part according to the statistical functionalities, i.e., the S3 generic functions. 57  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,

• Advanced, to discuss advanced topics and experimental features for expert R users.
• Benchmark, to compare computing cost using package microbenchmark (Mersmann 2024, 1.5.0).
• Data, to create and/or inspect R objects for subsequent operations.
• Example, to show how to use functions from these packages.
• Exception, to demonstrate the exception-handling mechanisms of functions from these packages.
• Figure, to generate elegant visualizations.
• Future, to list upcoming tasks for these packages.
• Previously, to copy R code from earlier chapters.
• Review, to demonstrate usage of functions from R version 4.5.2 (2025-10-31), or from contributed packages other than these packages.
• Table, to create HTML tables using function knitr::kable() (Xie 2025, v1.51).
• Workaround, to present alternative solutions to specific problems.

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

Mersmann, Olaf. 2024. microbenchmark: Accurate Timing Functions. https://CRAN.R-project.org/package=microbenchmark.

Myllymäki, Mari, and Tomáš Mrkvička. 2024. “GET: Global Envelopes in R.” Journal of Statistical Software 111 (3): 1–40. https://doi.org/10.18637/jss.v111.i03.

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/.