Grouped Hyper Data Frame

Author

Tingting Zhan

Published

March 16, 2026

Preface

This is an entertaining-but-useless project carried out primarily during Tingting Zhan’s leisure hours.

The author thanks

NoteContributors
  • Erjia Cui’s contribution to the function hyper.gam::hyper_gam().

The author present a collections of packages (these packages)

BibTeX and/or BibLaTeX entries for LaTeX users 
@Manual{,
  title = {groupedHyperframe: Grouped Hyper Data Frame},
  author = {Tingting Zhan},
  year = {2026},
  note = {R package version 0.3.7, commit 066bac2dc9b3ff2bce3241e39addd59ebe6b6644},
  url = {https://github.com/tingtingzhan/groupedHyperframe},
}

@Manual{,
  title = {groupedHyperframe.random: Simulated Grouped Hyper Data Frame},
  author = {Tingting Zhan},
  year = {2026},
  note = {R package version 0.2.4, commit cdbd027c234112f525e092863f197f8020e9a141},
  url = {https://github.com/tingtingzhan/groupedHyperframe.random},
}

@Manual{,
  title = {hyper.gam: Generalized Additive Models with Hyper Column},
  author = {Tingting Zhan},
  year = {2026},
  note = {R package version 0.2.4, commit 6595d33a81b6e112bcb5fe6b63ed3ad28e714259},
  url = {https://github.com/tingtingzhan/hyper.gam},
}

@Manual{,
  title = {maxEff: Additional Predictor with Maximum Effect Size},
  author = {Tingting Zhan},
  year = {2026},
  note = {R package version 0.2.4, commit e1ac58a52835f43ea6506bfd8c9397982d73dee7},
  url = {https://github.com/tingtingzhan/maxEff},
}

These packages require R version 4.5.0 (released 2025-04-11) or higher (macOS, Windows, Linux). Readers are encouraged to learn more about the full details of these packages in 6  About.

Listing 1 installs these packages from CRAN,

Listing 1: Install these packages from CRAN 
utils::install.packages('groupedHyperframe')
utils::install.packages('groupedHyperframe.random')
utils::install.packages('hyper.gam')
utils::install.packages('maxEff')

About This Book

Mirrors of this Quarto book can be accessed at the following URLs. These free hosting services may experience occasional downtime.

ImportantMirrors of this book

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

https://tingtingzhan-groupedhyperframe.netlify.app

This Quarto book documents

  • the creation of grouped hyper data frame (1  Grouped Hyper Data Frame);
  • the creation of a grouped hyper data frame with one-and-only-one point-pattern hypercolumn (Creation);
  • the batch process on eligible marks (Batch Process on Eligible Marks) for the one-and-only-one point-pattern hypercolumn in a (grouped) hyper data frame;
  • the computation of various summary statistics (Summarization) from one or more function-value-table hypercolumn(s) of a (grouped) hyper data frame;
  • the aggregation (Aggregation) of summary statistics, over a (nested) grouping structure, in a grouped hyper data frame.
  • the simulation of superimposed (marked) point-patterns via vectorized parameterization (Simulated Point-Pattern);
  • the simulation of grouped hyper data frame via matrix parameterization (Simulated Grouped Hyper Data Frame).

The Chapters 1  Grouped Hyper Data Frame, 2  Grouping ppp-Hypercolumn, 3  Simulation, 4  Quantile Index and 5  Predictor with Maximum Effect Size of this book explain how to use this package to a general audience.

Rest of this book explain why and how these packages works for readers with advanced expertise in the R programming language.