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 problems, and customizable arbitrary correlation structures. Options for fitting in parallel are provided. The method builds upon the framework described by Ezhov et al. (2018) <doi:10.1515/jag-2017-0029> using Lagrangian multipliers to fit cubic splines. For more information on correlation structure estimation, see Searle et al. (2009) <ISBN:978-0470009598>. For quadratic programming and constrained optimization in general, see Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. For a comprehensive background on smoothing splines, see Wahba (1990) <doi:10.1137/1.9781611970128> and Wood (2006) <ISBN:978-1584884743> "Generalized Additive Models: An Introduction with R".
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.