Package: gipsDA 1.0.0

Norbert Maksymilian Frydrysiak

gipsDA: Discriminant Analysis with Permutation-Invariant Covariance Models

Extends classical linear and quadratic discriminant analysis by incorporating permutation-group symmetries into covariance matrix estimation. Methods based on the 'gips' framework identify and impose permutation structures that regularize covariance estimates and improve stability and interpretability for symmetric or exchangeable features. The package provides pooled and class-specific covariance models, including multi-class variants with shared or independently estimated symmetry structures. The underlying methodology is described by Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen, and Kołodziejek (2025) <doi:10.18637/jss.v112.i07>.

Authors:Antoni Zbigniew Kingston [aut], Norbert Maksymilian Frydrysiak [aut, cre], Adam Przemysław Chojecki [ctb]

gipsDA_1.0.0.tar.gz
gipsDA_1.0.0.zip(r-4.7-any)gipsDA_1.0.0.zip(r-4.6-any)gipsDA_1.0.0.zip(r-4.5-any)
gipsDA_1.0.0.tgz(r-4.6-any)gipsDA_1.0.0.tgz(r-4.5-any)
gipsDA_1.0.0.tar.gz(r-4.7-any)gipsDA_1.0.0.tar.gz(r-4.6-any)
gipsDA_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gipsDA/json (API)

# Install 'gipsDA' in R:
install.packages('gipsDA', repos = c('https://antonikingston.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/antonikingston/gipsda/issues

Pkgdown/docs site:https://antonikingston.github.io

On CRAN:

Conda:

5.54 score 1 stars 6 scripts 461 downloads 3 exports 21 dependencies

Last updated from:1a54cbb267. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK3739
source / vignettesOK203
linux-releaseOK110
macos-releaseOK3787
macos-oldrelOK3795
windows-develOK79
windows-releaseOK71
windows-oldrelOK76
wasm-releaseOK105

Exports:gipsldagipsmultqdagipsqda

Dependencies:abinddigestdisordRfreealggipsgmpjsonlitelatticemagicMASSMatrixnumberspartitionspermutationspolynomrbibutilsRcppRdpackrlangsetsstringi

Advanced usage of gipsDA
Overview | Example data | Preparing data | Checking predictor types | Scaling predictors | Model choice | Model hierarchy | MAP: Maximum A Posteriori | Interpreting permutation output | Optimizer | max_iter | Class priors | weighted_avg in gipslda() | [S_ | [S_ | Formula interface | Matrix interface | Data-frame interface | Prediction output | Prediction methods for gipslda() | Leave-one-out prediction for QDA models | Inspecting fitted models | LDA diagnostics | Practical workflow | Troubleshooting | The model is slow | The output selects () | Predictions are identical across models | Training and test data have different preprocessing | Summary

Last update: 2026-10-01
Started: 2026-08-19

Getting started with gipsDA
Overview | Installation | Quick start | Preparing data | Model relationships | Choosing a model | Key arguments | MAP: Maximum A Posteriori | Optimizer | Fitting all three models | Inspecting a fitted model | Prediction output | Matrix interface | Summary

Last update: 2026-10-01
Started: 2026-08-04

Manual test
Purpose | Mock data in (\mathbb{R}^3) | Covariance projection | LDA reconstruction | Separate QDA reconstruction | Joint QDA reconstruction | Assertions

Last update: 2026-10-01
Started: 2026-07-27

Readme and manuals

Help Manual

Help pageTopics
Extract gips LDA discriminant coefficientscoef.gipslda
Linear Discriminant Analysis with gips Covariance Projectiongipslda gipslda.data.frame gipslda.default gipslda.formula gipslda.matrix model.frame.gipslda
Quadratic Discriminant Analysis with multiple gips-projected covariancesgipsmultqda gipsmultqda.data.frame gipsmultqda.default gipsmultqda.formula gipsmultqda.matrix model.frame.gipsmultqda
Quadratic Discriminant Analysis with gips covariance projectiongipsqda gipsqda.data.frame gipsqda.default gipsqda.formula gipsqda.matrix model.frame.gipsqda
Pairs plot for a fitted gips LDA modelpairs.gipslda
Plot a fitted gips LDA modelplot.gipslda
Predict from a gips LDA modelpredict.gipslda
Predict from a joint-projection gips QDA modelpredict.gipsmultqda
Predict from a gips QDA modelpredict.gipsqda
Print a fitted gipsDA modelprint.gipslda print.gipsmultqda print.gipsqda
Summarize a fitted gipsDA modelsummary.gipslda summary.gipsmultqda summary.gipsqda