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.
|Author||Nathaniel E. Helwig <[email protected]>|
|Date of publication||2017-02-03 14:32:57|
|Maintainer||Nathaniel E. Helwig <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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