lazymatrix: Perform Complex Matrix Operations Symbolically on Sparse Matrices

Provides a framework for lazy computation on large sparse matrices. Enables lazy evaluation of normalized data matrices, preserving sparsity throughout operations without materializing dense intermediate objects. Implements statistical algorithms including LSQR for sparse least squares as described in Paige and Saunders (1982) <doi:10.1145/355984.355989> and partial singular value decomposition via the augmented implicitly restarted Lanczos bidiagonalization algorithm of Baglama and Reichel (2005) <doi:10.1137/04060593X>.

Version:	0.1.0
Imports:	Matrix, methods, stats, irlba, Rcpp
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	bench, dplyr, ggplot2, knitr, rmarkdown, scales, testthat (≥ 3.0.0), tidyr
Published:	2026-07-14
DOI:	10.32614/CRAN.package.lazymatrix (may not be active yet)
Author:	Viktor Segersall [aut, cre, cph]
Maintainer:	Viktor Segersall <viktor.segersall at proton.me>
License:	GPL (≥ 3)
URL:	https://vsegersall.github.io/lazymatrix/
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	lazymatrix results

Documentation:

Reference manual:	lazymatrix.html , lazymatrix.pdf
Vignettes:	Getting started with LazyMatrix (source, R code) performance (source, R code) Use Cases for LazyMatrix: Statistical Algorithms (source, R code)

Downloads:

Package source:	lazymatrix_0.1.0.tar.gz
Windows binaries:	r-devel: lazymatrix_0.1.0.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): lazymatrix_0.1.0.tgz, r-oldrel (arm64): lazymatrix_0.1.0.tgz, r-release (x86_64): lazymatrix_0.1.0.tgz, r-oldrel (x86_64): lazymatrix_0.1.0.tgz

Linking:

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