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.

Package details

AuthorNathaniel E. Helwig <>
MaintainerNathaniel E. Helwig <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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bigsplines documentation built on May 2, 2019, 9:27 a.m.