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
Author:	Nathaniel E. Helwig
Maintainer:	Nathaniel E. Helwig <helwig at umn.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	ChangeLog
CRAN checks:	bigsplines results

Documentation:

Reference manual:

bigsplines.pdf

Downloads:

Package source:	bigsplines_1.1-1.tar.gz
Windows binaries:	r-devel: bigsplines_1.1-1.zip, r-release: bigsplines_1.1-1.zip, r-oldrel: bigsplines_1.1-1.zip
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

Linking:

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