bigsplines1.1-1 package

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.

  • Maintainer: Nathaniel E. Helwig
  • License: GPL (>= 2)
  • Last published: 2018-05-25