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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("bigsplines")

1.1-1 by Nathaniel E. Helwig, a year ago


Browse source code at https://github.com/cran/bigsplines


Authors: Nathaniel E. Helwig <[email protected]>


Documentation:   PDF Manual  


GPL (>= 2) license


Imports stats, graphics, grDevices

Depends on quadprog


Depended on by eegkit.


See at CRAN