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cran
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.
Maintained by Nathaniel E. Helwig. Last updated 7 years ago.
1.78 score 2 dependents