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cff-version: 1.2.0
message: 'To cite package "lgspline" in publications use:'
type: software
license: MIT
title: 'lgspline: Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation'
version: 1.1.0
doi: 10.32614/CRAN.package.lgspline
abstract: 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) <https://doi.org/10.1515/jag-2017-0029>. Quadratic-programming
  and SQP details follow Goldfarb & Idnani (1983) <https://doi.org/10.1007/BF02591962>
  and Nocedal & Wright (2006) <https://doi.org/10.1007/978-0-387-40065-5>. For smoothing
  spline and penalized spline background, see Wahba (1990) <https://doi.org/10.1137/1.9781611970128>
  and Wood (2017) <https://doi.org/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).
authors:
- family-names: Davis
  given-names: Matthew
  email: matthewlouisdavis@gmail.com
  orcid: https://orcid.org/0000-0001-9714-1018
repository: https://matthewlouisdavisbiostat.r-universe.dev
repository-code: https://github.com/matthewlouisdavisBioStat/lgspline
commit: 4a37d293f18a48d8ce85ae858481ad9f34e81816
url: https://github.com/matthewlouisdavisBioStat/lgspline
date-released: '2026-05-14'
contact:
- family-names: Davis
  given-names: Matthew
  email: matthewlouisdavis@gmail.com
  orcid: https://orcid.org/0000-0001-9714-1018