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.
| Version: |
1.1-1 |
| Depends: |
quadprog |
| Imports: |
stats, graphics, grDevices |
| Published: |
2018-05-25 |
| Author: |
Nathaniel E. Helwig |
| Maintainer: |
Nathaniel E. Helwig <helwig at umn.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Materials: |
ChangeLog |
| CRAN checks: |
bigsplines results |
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