Showing 5 of total 5 results (show query)
bwlewis
irlba:Fast Truncated Singular Value Decomposition and Principal Components Analysis for Large Dense and Sparse Matrices
Fast and memory efficient methods for truncated singular value decomposition and principal components analysis of large sparse and dense matrices.
Maintained by B. W. Lewis. Last updated 2 years ago.
pcaprincipal-component-analysissingular-value-decompositionsparse-principal-componentssvdopenblas
128 stars 13.85 score 1.5k scripts 293 dependents
bioc
BiocSingular:Singular Value Decomposition for Bioconductor Packages
Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.
Maintained by Aaron Lun. Last updated 5 months ago.
softwaredimensionreductionprincipalcomponentbioconductor-packagehuman-cell-atlassingular-value-decompositioncpp
7 stars 12.10 score 1.2k scripts 103 dependents
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
genridge:Generalized Ridge Trace Plots for Ridge Regression
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Maintained by Michael Friendly. Last updated 4 months ago.
bias-variancegraphicsprincipal-component-analysisregression-modelsridge-regressionsingular-value-decomposition
4 stars 4.84 score 69 scripts
rohelab
LRMF3:Low Rank Matrix Factorization S3 Objects
Provides S3 classes to represent low rank matrix decompositions.
Maintained by Alex Hayes. Last updated 3 years ago.
matrix-factorizationsingular-value-decomposition
2 stars 3.78 score 6 scripts 2 dependents