https://matthewlouisdavisbiostat.r-universe.devRecent package updates in matthewlouisdavisbiostatR-universehttps://github.com/matthewlouisdavisbiostat.pnghttps://matthewlouisdavisbiostat.r-universe.devThu, 14 May 2026 13:02:19 GMTmatthewlouisdavis@gmail.com (Matthew Davis)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).https://github.com/r-universe/matthewlouisdavisbiostat/actions/runs/25912713118Thu, 14 May 2026 13:02:19 GMTlgspline1.1.0successhttps://matthewlouisdavisbiostat.r-universe.devhttps://github.com/matthewlouisdavisbiostat/lgspline