Data smoothing with penalized splines is a popular method and is well established for one or twodimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrixfree implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product Bspline smoothing with multivariate and scattered data. It further provides matrixfree preconditioned versions of the CGalgorithm 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 matrixfree and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021).
Package details 


Author  Martin Siebenborn [aut, cre, cph], Julian Wagner [aut, cph] 
Maintainer  Martin Siebenborn <martin.siebenborn@unihamburg.de> 
License  MIT + file LICENSE 
Version  1.2 
Package repository  View on CRAN 
Installation 
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