R package hyper.gam

The complete vignette of R (R Core Team 2025) package hyper.gam (v0.2.1.20151215)

https://tingtingzhan.quarto.pub/groupedhyperframe/bioinformatics_btaf430.html

documents the derivation of single index predictors of scalar outcomes based on spatial and non-spatial single-cell imaging data.

The complete vignette exceeds the file size limit allowed on CRAN.

1 Prerequisite

1.1 Environment

Package hyper.gam (v0.2.1.20151215) requires R version 4.5.0 (released 2025-04-11) or higher (macOS, Windows, Linux).

An Integrated Development Environment (IDE), e.g., RStudio (Posit team 2025) or Positron, is not required, but highly recommended.

Sys.info()[c('sysname', 'release', 'machine')]
#>  sysname  release  machine 
#> "Darwin" "25.2.0"  "arm64"
R.version
#>                _                           
#> platform       aarch64-apple-darwin20      
#> arch           aarch64                     
#> os             darwin20                    
#> system         aarch64, darwin20           
#> status                                     
#> major          4                           
#> minor          5.2                         
#> year           2025                        
#> month          10                          
#> day            31                          
#> svn rev        88974                       
#> language       R                           
#> version.string R version 4.5.2 (2025-10-31)
#> nickname       [Not] Part in a Rumble

1.2 Enhancement & Dependency

Package hyper.gam (v0.2.1.20151215) Enhances package mgcv (Wood 2017, v1.9.4). Details are provided in the book, Section 7.1.

The dependencies of package hyper.gam are detailed in the book, Section 7.1.

Package hyper.gam requires the development versions of the spatstat.* family of packages (Baddeley, Rubak, and Turner 2015; Baddeley and Turner 2005). Installation instructions are provided in the book, Section 7.1.

1.3 Installation

Packages hyper.gam (v0.2.1.20151215) and groupedHyperframe (v0.3.2.20251225) can be installed using the following command.

utils::install.packages('groupedHyperframe')
utils::install.packages('hyper.gam')

2 Acknowledgement

Most of this work was undertaken during Tingting Zhan’s leisure hours, with limited support from National Institutes of Health, U.S. Department of Health and Human Services grants

• R01CA222847 (I. Chervoneva, T. Zhan, and H. Rui)
• R01CA253977 (H. Rui and I. Chervoneva).

The author thanks

• Erjia Cui’s contribution to function hyper_gam().

3 References

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.

Posit team. 2025. RStudio: Integrated Development Environment for R. Boston, MA: Posit Software, PBC. https://posit.co/.

R Core Team. 2025. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

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