SBMTrees: Sequential Imputation with Bayesian Trees Mixed-Effects Models for Longitudinal Data

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.

Version:	1.2
Depends:	R (≥ 4.1.0)
Imports:	Rcpp, lme4, Matrix, arm, dplyr, mvtnorm, sn, tidyr, mice, nnet
LinkingTo:	Rcpp, RcppArmadillo, RcppDist, RcppProgress
Suggests:	knitr, rmarkdown, mitml
Published:	2024-12-11
DOI:	10.32614/CRAN.package.SBMTrees
Author:	Jungang Zou [aut, cre], Liangyuan Hu [aut], Robert McCulloch [ctb], Rodney Sparapani [ctb], Charles Spanbauer [ctb]
Maintainer:	Jungang Zou <jungang.zou at gmail.com>
License:	GPL-2
NeedsCompilation:	yes
SystemRequirements:	GNU make
Materials:	README NEWS
CRAN checks:	SBMTrees results

Documentation:

Reference manual:	SBMTrees.pdf
Vignettes:	SBMTrees: Introduction and Usage (source, R code)

Downloads:

Package source:	SBMTrees_1.2.tar.gz
Windows binaries:	r-devel: SBMTrees_1.2.zip, r-release: SBMTrees_1.1.zip, r-oldrel: SBMTrees_1.2.zip
macOS binaries:	r-release (arm64): SBMTrees_1.2.tgz, r-oldrel (arm64): SBMTrees_1.2.tgz, r-release (x86_64): SBMTrees_1.2.tgz, r-oldrel (x86_64): SBMTrees_1.2.tgz
Old sources:	SBMTrees archive

Linking:

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