jmSurface: Semi-Parametric Association Surfaces for Joint
Longitudinal-Survival Models
Implements interpretable multi-biomarker fusion in joint
longitudinal-survival models via semi-parametric association surfaces.
Provides a two-stage estimation framework where Stage 1 fits mixed-effects
longitudinal models and extracts Best Linear Unbiased Predictors ('BLUP's),
and Stage 2 fits transition-specific penalized Cox models with
tensor-product spline surfaces linking latent biomarker summaries to
transition hazards. Supports multi-state disease processes with
transition-specific surfaces, Restricted Maximum Likelihood ('REML')
smoothing parameter selection, effective degrees of freedom ('EDF')
diagnostics, dynamic prediction of transition probabilities, and three
interpretability visualizations (surface plots, contour heatmaps,
marginal effect slices).
Methods are described in Bhattacharjee (2025, under review).
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
nlme, survival, mgcv, stats, utils, graphics, grDevices |
| Suggests: |
lme4, ggplot2, viridis, plotly, shiny, shinydashboard, dplyr, tidyr, testthat (≥ 3.0.0) |
| Published: |
2026-02-25 |
| DOI: |
10.32614/CRAN.package.jmSurface (may not be active yet) |
| Author: |
Atanu Bhattacharjee
 [aut, cre] |
| Maintainer: |
Atanu Bhattacharjee <atanustat at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
jmSurface results |
Documentation:
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