Package: ggpca 0.1.2

ggpca: Publication-Ready PCA, t-SNE, and UMAP Plots

Provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). This package helps visualize high-dimensional data with options for custom labels, density plots, and faceting, using the 'ggplot2' framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

Authors:Yaoxiang Li [cre, aut]

ggpca_0.1.2.tar.gz
ggpca_0.1.2.zip(r-4.5)ggpca_0.1.2.zip(r-4.4)ggpca_0.1.2.zip(r-4.3)
ggpca_0.1.2.tgz(r-4.4-any)ggpca_0.1.2.tgz(r-4.3-any)
ggpca_0.1.2.tar.gz(r-4.5-noble)ggpca_0.1.2.tar.gz(r-4.4-noble)
ggpca_0.1.2.tgz(r-4.4-emscripten)ggpca_0.1.2.tgz(r-4.3-emscripten)
ggpca.pdf |ggpca.html✨
ggpca/json (API)

# Install 'ggpca' in R:

install.packages('ggpca', repos = c('https://yaoxiangli.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yaoxiangli/ggpca/issues

On CRAN:

2.00 score 127 downloads 2 exports 71 dependencies

Last updated 13 days agofrom:b59189d9e0. Checks:OK: 5 NOTE: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 05 2024
R-4.5-win	OK	Nov 05 2024
R-4.5-linux	OK	Nov 05 2024
R-4.4-win	NOTE	Nov 05 2024
R-4.4-mac	NOTE	Nov 05 2024
R-4.3-win	OK	Nov 05 2024
R-4.3-mac	OK	Nov 05 2024