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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1-1 |
rolling linux/jammy R-4.5 | bigsplines_1.1-1.tar.gz |
568.4 KiB |
1.1-1 |
rolling linux/noble R-4.5 | bigsplines_1.1-1.tar.gz |
568.5 KiB |
1.1-1 |
rolling source/ R- | bigsplines_1.1-1.tar.gz |
103.9 KiB |
1.1-1 |
latest linux/jammy R-4.5 | bigsplines_1.1-1.tar.gz |
568.4 KiB |
1.1-1 |
latest linux/noble R-4.5 | bigsplines_1.1-1.tar.gz |
568.5 KiB |
1.1-1 |
latest source/ R- | bigsplines_1.1-1.tar.gz |
103.9 KiB |
1.1-1 |
2026-04-26 source/ R- | bigsplines_1.1-1.tar.gz |
103.9 KiB |
1.1-1 |
2026-04-23 source/ R- | bigsplines_1.1-1.tar.gz |
103.9 KiB |
1.1-1 |
2026-04-09 windows/windows R-4.5 | bigsplines_1.1-1.zip |
575.8 KiB |
1.1-1 |
2025-04-20 source/ R- | bigsplines_1.1-1.tar.gz |
103.9 KiB |