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".
Version: 0.2.0 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, graphics Published: 2025-04-24 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 CRAN checks: lgspline results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=lgspline to link to this page.
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