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.
| Version: |
0.1.1 |
| Depends: |
R (≥ 4.2.0) |
| Imports: |
stats, lme4, mgcv, dplyr, ggplot2, rlang |
| Suggests: |
lmerTest, knitr, rmarkdown, reformulas, testthat (≥ 3.0.0) |
| Published: |
2026-03-16 |
| DOI: |
10.32614/CRAN.package.MultiSpline (may not be active yet) |
| Author: |
Subir Hait [aut, cre] |
| Maintainer: |
Subir Hait <haitsubi at msu.edu> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
MultiSpline results |
Documentation:
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