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SBMTrees  

Longitudinal Sequential Imputation and Prediction with Bayesian Trees Mixed-Effects Models for Longitudinal Data
View on CRAN: Click here


Download and install SBMTrees package within the R console
Install from CRAN:
install.packages("SBMTrees")

Install from Github:
library("remotes")
install_github("cran/SBMTrees")

Install by package version:
library("remotes")
install_version("SBMTrees", "1.4")



Attach the package and use:
library("SBMTrees")
Maintained by
Jungang Zou
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-12-09
Latest Update:
Description:
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.
How to cite:
Jungang Zou (2024). SBMTrees: Longitudinal Sequential Imputation and Prediction with Bayesian Trees Mixed-Effects Models for Longitudinal Data. R package version 1.4, https://cran.r-project.org/web/packages/SBMTrees. Accessed 07 Mar. 2026.
Previous versions and publish date:
1.1 (2024-12-09 21:00), 1.2 (2024-12-11 08:50), 1.4 (2026-02-06 13:00)
Other packages that cited SBMTrees R package
View SBMTrees citation profile
Other R packages that SBMTrees depends, imports, suggests or enhances
Complete documentation for SBMTrees
Functions, R codes and Examples using the SBMTrees R package
Full SBMTrees package functions and examples
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