bigsplines: Smoothing Splines for Large Samples

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

Version: 1.0-4
Published: 2014-10-03
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

Downloads:

Reference manual: bigsplines.pdf
Package source: bigsplines_1.0-4.tar.gz
Windows binaries: r-devel: bigsplines_1.0-4.zip, r-release: bigsplines_1.0-4.zip, r-oldrel: bigsplines_1.0-4.zip
OS X Snow Leopard binaries: r-release: bigsplines_1.0-4.tgz, r-oldrel: bigsplines_1.0-4.tgz
OS X Mavericks binaries: r-release: bigsplines_1.0-4.tgz
Old sources: bigsplines archive

Reverse dependencies:

Reverse depends: eegkit