Package: sigPCA 0.1.0

sigPCA: Statistical Significance Testing for Principal Components

Identifies principal components whose eigenvalues exceed those expected under noise. Implements analytical thresholds derived from the Marchenko-Pastur distribution (Marchenko and Pastur, 1967) <doi:10.1070/SM1967v001n04ABEH001994> and empirical permutation tests, and provides functions for visualizing observed and null eigenvalue spectra.

Authors:Guillermo de Anda-Jáuregui [aut, cre, cph], Enrique Hernández-Lemus [aut, cph]

sigPCA_0.1.0.tar.gz
sigPCA_0.1.0.zip(r-4.7-any)sigPCA_0.1.0.zip(r-4.6-any)sigPCA_0.1.0.zip(r-4.5-any)
sigPCA_0.1.0.tgz(r-4.6-any)sigPCA_0.1.0.tgz(r-4.5-any)
sigPCA_0.1.0.tar.gz(r-4.7-any)sigPCA_0.1.0.tar.gz(r-4.6-any)
sigPCA_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
sigPCA/json (API)

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

Bug tracker:https://github.com/guillermodeandajauregui/sigpca/issues

On CRAN:

Conda:

4.48 score 1 stars 3 scripts 189 downloads 7 exports 17 dependencies

Last updated from:5c84fbf6c8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK149
source / vignettesOK180
linux-releaseOK156
macos-releaseOK160
macos-oldrelOK155
windows-develOK79
windows-releaseOK93
windows-oldrelOK79
wasm-releaseOK119

Exports:compute_eigenvaluesmarchenko_pastur_boundsplot_sigPCAplot_sigPCA_histogramsigPCAsigPCA_mpsigPCA_perm

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Finding structure in iris with sigPCA
Overview | Prepare the measurements | Test the eigenvalue spectrum | Interpret the retained component | What about eigenvalues below the lower bound? | Practical considerations | References
Rendered fromsigPCA-iris.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-07-23

Finding structure in Palmer penguins with sigPCA
Overview | Start from the morphological measurements | Fit the conventional PCA | Test the eigenvalue spectrum | Interpret the retained structure | What about eigenvalues below the lower bound? | Practical considerations | References
Rendered fromsigPCA-penguins.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-07-23

sigPCA: Statistical Significance Testing for Principal Components
Introduction | Basic Example: White Noise | Example: Latent Signal | Optional: Permutation-Based p-values | Interpretation | References
Rendered fromsigPCA.Rmdusingknitr::rmarkdown

Last update: 2025-07-23
Started: 2025-07-23