BCClong: Bayesian Consensus Clustering for Multiple Longitudinal Features

It is very common nowadays for a study to collect multiple features and appropriately integrating multiple longitudinal features simultaneously for defining individual clusters becomes increasingly crucial to understanding population heterogeneity and predicting future outcomes. 'BCClong' implements a Bayesian consensus clustering (BCC) model for multiple longitudinal features via a generalized linear mixed model. Compared to existing packages, several key features make the 'BCClong' package appealing: (a) it allows simultaneous clustering of mixed-type (e.g., continuous, discrete and categorical) longitudinal features, (b) it allows each longitudinal feature to be collected from different sources with measurements taken at distinct sets of time points (known as irregularly sampled longitudinal data), (c) it relaxes the assumption that all features have the same clustering structure by estimating the feature-specific (local) clusterings and consensus (global) clustering.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: cluster, coda, ggplot2, graphics, label.switching, LaplacesDemon, lme4, MASS, mclust, MCMCpack, mixAK, mvtnorm, nnet, Rcpp (≥ 1.0.9), Rmpfr, stats, truncdist
LinkingTo: Rcpp, RcppArmadillo
Suggests: cowplot, joineRML, knitr, rmarkdown, survival, survminer, testthat (≥ 3.0.0)
Published: 2023-01-13
Author: Zhiwen Tan [aut, cre], Zihang Lu [ctb], Chang Shen [ctb]
Maintainer: Zhiwen Tan <21zt9 at queensu.ca>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: BCClong citation info
CRAN checks: BCClong results


Reference manual: BCClong.pdf
Vignettes: ContinuousData


Package source: BCClong_1.0.0.tar.gz
Windows binaries: r-devel: BCClong_1.0.0.zip, r-release: BCClong_1.0.0.zip, r-oldrel: BCClong_1.0.0.zip
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available


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