Package: MultiSpline 0.3.0

MultiSpline: Spline-Based Nonlinear Modeling for Multilevel and Longitudinal Data

Provides a unified framework for fitting, predicting, and interpreting nonlinear relationships in single-level, multilevel, and longitudinal regression models. Flexible functional forms are supported using natural cubic splines ('splines'), B-splines ('splines'), and GAM smooths ('mgcv'). Supports two-way and nested clustering via 'lme4', automatic knot selection by AIC or BIC, multilevel R-squared decomposition (marginal and conditional R-squared of Nakagawa and Schielzeth (2013) <doi:10.1111/j.2041-210x.2012.00261.x>, extended to random slopes as in Johnson (2014) <doi:10.1111/2041-210X.12225>, with level-specific variance partitioning), a postestimation suite returning first and second derivatives with confidence bands, turning points and inflection regions, and a model comparison workflow contrasting linear, polynomial, and spline fits by AIC, BIC, and likelihood-ratio tests. Cluster heterogeneity in nonlinear effects is supported via random-slope spline terms. Any 'lme4' random-effects structure can be supplied, and population-averaged (marginal) curves are available for generalized linear mixed models.

Authors:Subir Hait [aut, cre]