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erichson
rsvd:Randomized Singular Value Decomposition
Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.
Maintained by N. Benjamin Erichson. Last updated 4 years ago.
dimension-reductionmatrix-approximationpcaprincipal-component-analysisprobabilistic-algorithmsrandomized-algorithmsingular-value-decompositionsvd
99 stars 10.88 score 408 scripts 124 dependents
friendly
ggbiplot:A Grammar of Graphics Implementation of Biplots
A 'ggplot2' based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using 'ggplot2' techniques.
Maintained by Michael Friendly. Last updated 6 months ago.
biplotdata-visualizationdimension-reductionprincipal-component-analysis
12 stars 8.15 score 2.4k scripts 1 dependents
klauschn
ICtest:Estimating and Testing the Number of Interesting Components in Linear Dimension Reduction
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
Maintained by Klaus Nordhausen. Last updated 3 years ago.
4.36 score 63 scripts 4 dependents