Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation R package lgspline version 1.1.0 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) <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). Software Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.7), RcppArmadillo, FNN, RColorBrewer, plotly, quadprog, methods, stats LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), spelling, knitr, rmarkdown, parallel, survival, MASS, graphics Matthew Davis <matthewlouisdavis@gmail.com> Comprehensive R Archive Network (CRAN) Matthew Davis [aut, cre] (ORCID: <https://orcid.org/0000-0001-9714-1018>) MIT + file LICENSE (https://CRAN.R-project.org/package=lgspline/LICENSE) 2026-05-08 application/tgz https://CRAN.R-project.org/package=lgspline doi:10.32614/CRAN.package.lgspline en-US