irlba

R package 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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 7 articles )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Avron, Haim; Druinsky, Alex; Toledo, Sivan: Spectral condition-number estimation of large sparse matrices. (2019)
  2. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  3. N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH
  4. Alfonso Iodice D’Enza, Angelos Markos, Davide Buttarazzi: The idm Package: Incremental Decomposition Methods in R (2018) not zbMATH
  5. Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv
  6. Michael Kane; John Emerson; Stephen Weston: Scalable Strategies for Computing with Massive Data (2013) not zbMATH
  7. Fabian Scheipl: spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R (2011) not zbMATH