Title: Data-Informed Link Strength. Combine multiple-relationship
networks into a single weighted network. Impute (fill-in)
Description: Combine multiple-relationship networks into a single weighted
network.  The approach is similar to factor analysis in the that
contribution from each constituent network varies so as to maximize the
information gleaned from the multiple-relationship networks.
This implementation uses Principal Component Analysis calculated using
'prcomp' with bootstrap subsampling.  Missing links are imputed using

Title: A Framework for Dimensionality Reduction
Description: A collection of dimensionality reduction
techniques from R packages and provides a common
Suggests: MASS, Matrix, RANN, RSpectra, Rtsne, coRanking, diffusionMap,
energy, fastICA, ggplot2, graphics, igraph, kernlab, lle, loe,
optimx, pcaPP, scales, scatterplot3d, stats, testthat, tidyr,
'dimRed.R' 'drr.R' 'embed.R' 'fastica.R' 'graph_embed.R'
'hlle.R' 'isomap.R' 'kpca.R' 'leim.R' 'lle.R' 'loe.R' 'mds.R'
'mixColorSpaces.R' 'nmds.R' 'pca.R' 'plot.R' 'quality.R'

optimx, pcaPP, rgl, scales, scatterplot3d, stats, testthat,
'dimRed.R' 'drr.R' 'embed.R' 'fastica.R' 'get_info.R'
'graph_embed.R' 'hlle.R' 'isomap.R' 'kpca.R' 'leim.R' 'lle.R'
'loe.R' 'mds.R' 'mixColorSpaces.R' 'nmds.R' 'pca.R' 'plot.R'

Suggests: cccd, MASS, Matrix, RANN, RSpectra, Rtsne, coRanking,
diffusionMap, energy, fastICA, ggplot2, graphics, igraph,
keras, kernlab, knitr, lle, loe, optimx, pcaL1, pcaPP,
reticulate, rgl, scales, scatterplot3d, stats, tensorflow,
'graph_embed.R' 'hlle.R' 'isomap.R' 'kpca.R' 'l1pca.R' 'leim.R'
'lle.R' 'loe.R' 'mds.R' 'mixColorSpaces.R' 'nmds.R' 'nnmf.R'
'pca.R' 'plot.R' 'quality.R' 'rotate.R' 'soe.R' 'tsne.R'

Imports: NMF, magrittr, methods, bigmemory, Biobase

Suggests: NMF, MASS, Matrix, RANN, RSpectra, Rtsne, cccd, coRanking,

keras, kernlab, knitr, loe, optimx, pcaL1, pcaPP, reticulate,
rgl, scales, scatterplot3d, stats, tensorflow, testthat, tidyr,

Description: Simple Principal Components Analysis (PCA) and
Correspondence Analysis (CA) based on the Singular Value Decomposition
(SVD). This package provides S4 classes and methods to compute,
extract, summarize and visualize results of multivariate data
analysis. It also includes methods for partial bootstrap validation
described in Greenacre (1984) <isbn: 978-0-12-299050-2> and Lebart et al.
Suggests: folio, FactoMineR, ggrepel, khroma, testthat (>= 3.0.0),
Collate: 'AllClasses.R' 'AllGenerics.R' 'bootstrap.R' 'ca.R'
'dimensio-package.R' 'helpers.R' 'joint.R' 'mutators.R' 'pca.R'
'plot.R' 'show.R' 'subset.R' 'summary.R' 'zzz.R'