Package: gips 1.2.3.9000

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:
gips_1.2.3.9000.tar.gz
gips_1.2.3.9000.zip(r-4.5)gips_1.2.3.9000.zip(r-4.4)gips_1.2.3.9000.zip(r-4.3)
gips_1.2.3.9000.tgz(r-4.5-any)gips_1.2.3.9000.tgz(r-4.4-any)gips_1.2.3.9000.tgz(r-4.3-any)
gips_1.2.3.9000.tar.gz(r-4.5-noble)gips_1.2.3.9000.tar.gz(r-4.4-noble)
gips_1.2.3.9000.tgz(r-4.4-emscripten)gips_1.2.3.9000.tgz(r-4.3-emscripten)
gips.pdf |gips.html✨
gips/json (API)
NEWS
# 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 site:https://przechoj.github.io
covariance-estimationmachine-learningnormal-distribution
Last updated 8 days agofrom:7b7841a64b. Checks:9 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 18 2025 |
R-4.5-win | OK | Mar 18 2025 |
R-4.5-mac | OK | Mar 18 2025 |
R-4.5-linux | OK | Mar 18 2025 |
R-4.4-win | OK | Mar 18 2025 |
R-4.4-mac | OK | Mar 18 2025 |
R-4.4-linux | OK | Mar 18 2025 |
R-4.3-win | OK | Mar 18 2025 |
R-4.3-mac | OK | Mar 18 2025 |
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:abinddigestdisordRfreealggmplatticemagicmathjaxrMatrixnumberspartitionspermutationspolynomrbibutilsRcppRdpackrlangsets
A Gentle Introduction to Modeling with gips
Rendered fromgips.Rmd
usingknitr::rmarkdown
on Mar 18 2025.Last update: 2025-03-17
Started: 2022-09-14
Available Optimizers: How to Find Maximum A Posteriori?
Rendered fromOptimizers.Rmd
usingknitr::rmarkdown
on Mar 18 2025.Last update: 2025-03-17
Started: 2022-09-06
The Theory Behind gips
Rendered fromTheory.Rmd
usingknitr::rmarkdown
on Mar 18 2025.Last update: 2025-03-17
Started: 2022-09-13