Description
Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation.
Description
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).
README.md
Introduction
This R package implements Lagrangian multiplier smoothing splines, which reformulate smoothing splines through constrained optimization. This approach provides direct access to predictor-response relationships through interpretable coefficients, unlike other formulations that require post-fitting algebraic manipulation.
Installation
devtools::install_github("matthewlouisdavisBioStat/lgspline")
Citation
If you use this package in your research, please cite:
Davis, M. (2025). Lagrangian Multiplier Smoothing Splines. https://github.com/matthewlouisdavisBioStat/lgspline/
Contact
For questions or feedback, please open an issue on GitHub.