SBMTrees
1.5Longitudinal Sequential Imputation and Prediction with Bayesian Trees Mixed-Effects Models for Longitudinal Data
Overview
Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.
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- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-2511 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-04-2211 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- ERROR2026-04-1810 OK · 3 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-04-108 OK · 6 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-097 OK · 6 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1011 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
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- Return-value docs
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- References docs
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Package metadata
- First published
- 2024-12-09
- Total releases
- 4 / 2 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 134 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet