bigsplines: Smoothing Splines for Large Samples

Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Version: 1.1-1
Depends: quadprog
Imports: stats, graphics, grDevices
Published: 2018-05-25
DOI: 10.32614/CRAN.package.bigsplines
Author: Nathaniel E. Helwig
Maintainer: Nathaniel E. Helwig <helwig at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: bigsplines results


Reference manual: bigsplines.pdf


Package source: bigsplines_1.1-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bigsplines_1.1-1.tgz, r-oldrel (arm64): bigsplines_1.1-1.tgz, r-release (x86_64): bigsplines_1.1-1.tgz, r-oldrel (x86_64): bigsplines_1.1-1.tgz
Old sources: bigsplines archive

Reverse dependencies:

Reverse depends: eegkit
Reverse imports: fcfdr


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