https://spectralib.org/> for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user.">

RSpectra: Solvers for Large-Scale Eigenvalue and SVD Problems (original) (raw)

Reverse imports:

APL, bamm, bigstatsr, bigutilsr, blapsr, bspcov, castor, ClimMobTools, cvCovEst, ddpca, destiny, dtwclust, e2tree, fase, fastadi, fastRG, filling, funcharts, fungible, fuser, gasper, GhostKnockoff, Gmedian, greed, gsbm, HCD, HDANOVA, HDMFA, HiContacts, Holomics, HyperG, IALS, ideanet, ILoReg, kbal, knockoff, KOFM, lfa, lfmm, locStra, LSX, maotai, markerpen, MatrixCorrelation, mina, miRNAss, missSBM, motifcluster, multiblock, multiness, multivarious, NetworkDistance, NetworkReg, nipalsMCIA, OLCPM, OPTtesting, pcadapt, phylter, PlackettLuce, qlcVisualize, quanteda.textmodels, rags2ridges, rainette, randnet, rARPACK, Rdimtools, Rfssa, RGMM, rhype, RMLPCA, RobRegression, rrda, scPCA, scTenifoldKnk, scTenifoldNet, SDPDmod, Seurat, SGCP, sgdGMF, SIMLR, SISIR, smartsnp, sparsegl, sparseLRMatrix, SpectralClMixed, SplitKnockoff, tall, textmineR, TopicScore, ubms, umap, uSORT, uwot, veloviz, Vicus, Voyager, vsp, wordvector