lgspline
Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation
Implements Lagrangian multiplier smoothing splines for flexible nonparametric regression and function estimation. Provides tools for fitting, prediction, and inference using a constrained optimization approach to enforce smoothness. Supports generalized linear models, Weibull accelerated failure time (AFT) models, quadratic programming constraints, and customizable working-correlation structures, with options for parallel fitting. The core spline construction builds on Ezhov et al. (2018) <doi:10.1515/jag-2017-0029>. Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) <doi:10.1007/BF02591962> and Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. For smoothing spline and penalized spline background, see Wahba (1990) <doi:10.1137/1.9781611970128> and Wood (2017) <doi:10.1201/9781315370279>. For variance-component and correlation-parameter estimation, see Searle et al. (2006) <ISBN:978-0470009598>. The default multivariate partitioning step uses k-means clustering as in MacQueen (1967).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.2 |
rolling linux/jammy R-4.5 | lgspline_1.0.2.tar.gz |
1.4 MiB |
1.0.2 |
rolling linux/noble R-4.5 | lgspline_1.0.2.tar.gz |
1.4 MiB |
1.0.2 |
rolling source/ R- | lgspline_1.0.2.tar.gz |
462.4 KiB |
1.0.2 |
latest linux/jammy R-4.5 | lgspline_1.0.2.tar.gz |
1.4 MiB |
1.0.2 |
latest linux/noble R-4.5 | lgspline_1.0.2.tar.gz |
1.4 MiB |
1.0.2 |
latest source/ R- | lgspline_1.0.2.tar.gz |
462.4 KiB |
1.0.2 |
2026-04-26 source/ R- | lgspline_1.0.2.tar.gz |
462.4 KiB |
1.0.2 |
2026-04-23 source/ R- | lgspline_1.0.2.tar.gz |
462.4 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | lgspline_1.0.1.zip |
1.7 MiB |