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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 |
| Reference manual: | bigsplines.pdf |
| Package source: | bigsplines_1.1-1.tar.gz |
| Windows binaries: | r-devel: bigsplines_1.1-1.zip, r-release: bigsplines_1.1-1.zip, r-oldrel: bigsplines_1.1-1.zip |
| macOS binaries: | r-release (arm64): bigsplines_1.1-1.tgz, r-oldrel (arm64): bigsplines_1.1-1.tgz, r-release (x86_64): bigsplines_1.1-1.tgz, r-oldrel (x86_64): bigsplines_1.1-1.tgz |
| Old sources: | bigsplines archive |
| Reverse depends: | eegkit |
| Reverse imports: | fcfdr |
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