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

Provides tools for fitting, predicting, and visualizing nonlinear relationships in single-level, multilevel, and longitudinal regression models. Nonlinear functional forms are represented using natural cubic splines from 'splines' and smooth terms from 'mgcv'. The package offers a unified interface for specifying nonlinear effects, interactions with time variables, random-intercept clustering structures, and additional linear covariates. Utilities are included to generate prediction grids and produce effect plots, facilitating interpretation and visualization of nonlinear relationships in applied regression workflows. The implementation builds on established methods for spline-based regression and mixed-effects modeling (Hastie and Tibshirani, 1990 <doi:10.1201/9780203738535>; Bates et al., 2015 <doi:10.18637/jss.v067.i01>; Wood, 2017 <doi:10.1201/9781315370279>). Applications include hierarchical and longitudinal data structures common in education, health, and social science research.

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