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.


Reference manual

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

Browse source code at

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