lgspline-package: Lagrangian Multiplier Smoothing Splines

In lgspline: Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation

Lagrangian Multiplier Smoothing Splines

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, Cox proportional hazards models, quadratic programming constraints, and customizable working-correlation structures, with options for parallel fitting. The core spline construction builds on Ezhov et al. (2018) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1515/jag-2017-0029")}. Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02591962")} and Nocedal & Wright (2006) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-0-387-40065-5")}. For smoothing spline and penalized spline background, see Wahba (1990) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1137/1.9781611970128")} and Wood (2017) \Sexpr[results=rd]{tools:::Rd_expr_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).

Author(s)

Maintainer: Matthew Davis [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/matthewlouisdavisBioStat/lgspline

• Report bugs at https://github.com/matthewlouisdavisBioStat/lgspline/issues

lgspline documentation built on May 8, 2026, 5:07 p.m.