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 (2019) <arXiv:1901.00654>.
| Version: | 1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 1.0.5), combinat (≥ 0.0-8), statmod (≥ 1.1) |
| LinkingTo: | Rcpp |
| Suggests: | testthat |
| Published: | 2021-01-08 |
| Author: | Martin Siebenborn [aut, cre, cph], Julian Wagner [aut, cph] |
| Maintainer: | Martin Siebenborn <martin.siebenborn at uni-hamburg.de> |
| BugReports: | https://github.com/SplineSmoothing/MGSS |
| License: | MIT + file LICENSE |
| URL: | https://arxiv.org/abs/1901.00654 |
| NeedsCompilation: | yes |
| CRAN checks: | mgss results |
| Reference manual: | mgss.pdf |
| Package source: | mgss_1.0.tar.gz |
| Windows binaries: | r-devel: mgss_1.0.zip, r-release: mgss_1.0.zip, r-oldrel: mgss_1.0.zip |
| macOS binaries: | r-release: mgss_1.0.tgz, r-oldrel: mgss_1.0.tgz |
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