Grouped Hyper Data Frame

Author

Tingting Zhan

Published

March 18, 2026

Preface

ImportantDisclaimer

Active development of these R packages (Note 1) is temporarily suspended. Until further notice, these packages should

  • not be used as a basis for research grant applications,
  • not be cited as an actively maintained tool in a peer-reviewed manuscript,
  • not be used to support or fulfill requirements for pursuing an academic degree.

In addition, work primarily based on these packages (Note 1) should not be presented at academic conferences or similar scholarly venues.

Furthermore, a person’s ability to use these packages (Note 1) does not necessarily indicate an understanding of their underlying mechanisms. Accordingly, demonstration of their use alone should not be considered sufficient evidence of expertise, nor should it be credited as a basis for academic promotion or advancement.

These statements are advisory in nature and do not modify or restrict the rights granted under the GNU General Public License https://www.r-project.org/Licenses/.

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

The author thanks

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

The author present a collections of packages (these packages, Note 1)

Note 1: 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 428225aed784ddee4859cc1d926aa3bdd2a97ac2},
  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 578f9e31202d7653d036b2b0e1c0a4c2619924d9},
  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 82fee55e89e4b3f2727fabbdb2158ee937ae027d},
  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 41b65971a8b44b2a0f2fd05ef090293908901db3},
  url = {https://github.com/tingtingzhan/maxEff},
}

Installation

These packages (Note 1) 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. Chapter 7  Development Version explains how to install these packages from Github.

Listing 1: Install these packages from CRAN These packages are archived on CRAN at author’s request 
# 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  Generalized Additive Model (GAM) based on Quantiles 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.