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, 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).
| Version: |
1.1.0 |
| 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 |
| Published: |
2026-05-08 |
| DOI: |
10.32614/CRAN.package.lgspline |
| Author: |
Matthew Davis 
[aut, cre] |
| Maintainer: |
Matthew Davis <matthewlouisdavis at gmail.com> |
| BugReports: |
https://github.com/matthewlouisdavisBioStat/lgspline/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/matthewlouisdavisBioStat/lgspline |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
lgspline results |
Documentation:
Downloads:
Linking:
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https://CRAN.R-project.org/package=lgspline
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