Package: vsp 0.1.4

Alex Hayes

vsp: Vintage Sparse PCA for Semi-Parametric Factor Analysis

Provides fast spectral estimation of latent factors in random dot product graphs using the vsp estimator. Under mild assumptions, the vsp estimator is consistent for (degree-corrected) stochastic blockmodels, (degree-corrected) mixed-membership stochastic blockmodels, and degree-corrected overlapping stochastic blockmodels.

Authors:Karl Rohe [aut], Muzhe Zeng [aut], Alex Hayes [aut, cre, cph], Fan Chen [aut]

vsp_0.1.4.tar.gz
vsp_0.1.4.zip(r-4.7)vsp_0.1.4.zip(r-4.6)vsp_0.1.4.zip(r-4.5)
vsp_0.1.4.tgz(r-4.6-any)vsp_0.1.4.tgz(r-4.5-any)
vsp_0.1.4.tar.gz(r-4.7-any)vsp_0.1.4.tar.gz(r-4.6-any)
vsp_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
vsp/json (API)

# Install 'vsp' in R:

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

Bug tracker:https://github.com/rohelab/vsp/issues

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

On CRAN:

6.54 score 26 stars 22 scripts 541 downloads 27 exports 47 dependencies

Last updated from:0898a6a874. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	175
source / vignettes	OK	217
linux-release-x86_64	OK	177
macos-release-arm64	OK	153
macos-oldrel-arm64	OK	120
windows-devel	OK	129
windows-release	OK	147
windows-oldrel	OK	118
wasm-release	OK	143

Exports:%>%bff bind_svd_u bind_svd_v bind_varimax_y bind_varimax_z cumulative_participation get_svd_u get_svd_v get_varimax_y get_varimax_z get_y_hubs get_z_hubs ipr iprs localization_statistics plot_cumulative_curves plot_ipr_curves plot_ipr_pairs plot_mixing_matrix plot_svd_u plot_svd_v plot_varimax_y_pairs plot_varimax_z_pairs set_y_factor_names set_z_factor_names vsp

Dependencies:cli clue cluster codetools cpp11 digest dplyr farver furrr future generics ggplot2 globals glue gtable igraph invertiforms isoband labeling lattice lifecycle listenv LRMF3 magrittr Matrix parallelly pillar pkgconfig purrr R6 RColorBrewer Rcpp RcppEigen rlang RSpectra S7 scales sparseLRMatrix stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Interpreting factors with bff(), the Best Feature Function

Intro | Methodology | Implementation

Rendered frombff.Rmdusingknitr::rmarkdown

Last update: 2024-11-04
Started: 2020-01-31

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Find features most associated with cluster membership	bff
Add Z factor loadings to node table of tidygraph	bind_svd_u bind_svd_v bind_varimax_y bind_varimax_z
Calculate cumulative participation of a set of singular vectors.	cumulative_participation
Get left singular vectors in a tibble	get_svd_u get_svd_v get_varimax_y get_varimax_z
Get most important hubs for each Z factor	get_y_hubs get_z_hubs
Calculate the inverse participation ratio (IPR) for a vector.	ipr
Calculate IPR for all singular vectors in a list.	iprs
Compute localization statistics across various regularization parameters.	localization_statistics
Plot cumulative participation curves.	plot_cumulative_curves
Plot IPR curves.	plot_ipr_curves
Plot pairs of inverse participation ratios for singular vectors	plot_ipr_pairs
Plot the mixing matrix B	plot_mixing_matrix
Create a pairs plot of select Y factors	plot_svd_u plot_svd_v plot_varimax_y_pairs plot_varimax_z_pairs
Create a screeplot from a factor analysis object	screeplot.vsp_fa
Give the dimensions of Z factors informative names	set_y_factor_names set_z_factor_names
Semi-Parametric Factor Analysis via Vintage Sparse PCA	vsp vsp.default vsp.dgCMatrix vsp.igraph vsp.Matrix vsp.matrix
Create a vintage sparse factor analysis object	vsp_fa
Perform varimax rotation on a low rank matrix factorization	vsp.svd_like