mgss: A Matrix-Free Multigrid Preconditioner for Spline Smoothing

Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021).

Version: 1.2
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.5), combinat (≥ 0.0-8), statmod (≥ 1.1), Matrix (≥ 1.2)
LinkingTo: Rcpp
Suggests: testthat
Published: 2021-05-10
DOI: 10.32614/CRAN.package.mgss
Author: Martin Siebenborn [aut, cre, cph], Julian Wagner [aut, cph]
Maintainer: Martin Siebenborn <martin.siebenborn at>
License: MIT + file LICENSE
NeedsCompilation: yes
CRAN checks: mgss results


Reference manual: mgss.pdf


Package source: mgss_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mgss_1.2.tgz, r-oldrel (arm64): mgss_1.2.tgz, r-release (x86_64): mgss_1.2.tgz, r-oldrel (x86_64): mgss_1.2.tgz
Old sources: mgss archive


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