Package: gips 1.3.0

Adam Przemysław Chojecki

gips: Gaussian Model Invariant by Permutation Symmetry

Find the permutation symmetry group such that the covariance matrix of the given data is approximately invariant under it. Discovering such a permutation decreases the number of observations needed to fit a Gaussian model, which is of great use when it is smaller than the number of variables. Even if that is not the case, the covariance matrix found with 'gips' approximates the actual covariance with less statistical error. The methods implemented in this package are described in Graczyk et al. (2022) <doi:10.1214/22-AOS2174>. Documentation about 'gips' is provided via its website at <https://przechoj.github.io/gips/> and the paper by Chojecki, Morgen, Kołodziejek (2025, <doi:10.18637/jss.v112.i07>).

Authors:Adam Przemysław Chojecki [aut, cre], Paweł Morgen [aut], Bartosz Kołodziejek [aut]

gips_1.3.0.tar.gz
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gips_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gips/json (API)

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

Bug tracker:https://github.com/przechoj/gips/issues

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

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

covariance-estimationmachine-learningnormal-distributioncpp

7.39 score 10 stars 1 packages 30 scripts 559 downloads 16 exports 18 dependencies

Last updated from:09f99bea7c. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK151
linux-devel-x86_64OK171
source / vignettesOK237
linux-release-arm64OK136
linux-release-x86_64OK162
macos-release-arm64OK218
macos-release-x86_64OK325
macos-oldrel-arm64OK201
macos-oldrel-x86_64OK438
windows-devel-arm64OK373
windows-devel-x86_64OK160
windows-release-arm64OK352
windows-release-x86_64OK150
windows-oldrel-x86_64OK108
wasm-releaseOK125

Exports:calculate_gamma_functioncompare_log_posteriories_of_permscompare_posteriories_of_permsfind_MAPforget_permsget_probabilities_from_gipsget_structure_constantsgipsgips_permlog_posteriori_of_gipsnew_gipsnew_gips_permprepare_orthogonal_matrixproject_matrixvalidate_gipsvalidate_gips_perm

Dependencies:abinddigestdisordRfreealggmplatticemagicMatrixnumberspartitionspermutationspolynomrbibutilsRcppRdpackrlangsetsstringi

A Gentle Introduction to Modeling with gips
The problem | Invariance by permutation | Package gips | Practical example | Theoretic example | Multi-sample example | Further reading
Rendered fromgips.Rmdusingknitr::rmarkdown

Last update: 2026-07-25
Started: 2022-09-14

The Theory Behind gips
What the gips is based on | Alternative reference | Basic definitions | Block Decomposition - [1], Theorem 1 | Examples | Project Matrix - [1, Eq. (6)] | Trivial case | Notation | Example | $C_\sigma$ and n0 | Bayesian model selection | General workflow | Details on the prior distribution | gips technical details | Interpretation | Finding the MAP Estimator | Generate example data from a model: | End of prepare model | Information Criterion - AIC and BIC | Estimated mean | References
Rendered fromTheory.Rmdusingknitr::rmarkdown

Last update: 2026-07-25
Started: 2022-09-13

Available Optimizers: How to Find Maximum A Posteriori?
What are we optimizing? | Available optimizers | Note on computation time | Brute Force | Metropolis-Hastings | Short description | Notes | Example | Hill climbing | Pseudocode | Continuing the optimization | sigma_matrix is the real covariance matrix, that we want to estimate | Additional parameters | Discussion | References
Rendered fromOptimizers.Rmdusingknitr::rmarkdown

Last update: 2026-07-25
Started: 2022-09-06