Package: BCClong 1.0.3
Zhiwen Tan
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.
Authors:
BCClong_1.0.3.tar.gz
BCClong_1.0.3.tar.gz(r-4.5-noble)BCClong_1.0.3.tar.gz(r-4.4-noble)
BCClong_1.0.3.tgz(r-4.4-emscripten)BCClong_1.0.3.tgz(r-4.3-emscripten)
BCClong.pdf |BCClong.html✨
BCClong/json (API)
| # Install 'BCClong' in R: |
| install.packages('BCClong', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 months agofrom:adbd93d49b. Checks:2 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 21 2025 |
| R-4.5-linux-x86_64 | OK | Jan 21 2025 |
Exports:BayesTBCC.multimodel.selection.criteriatraceplottrajplot
Dependencies:abindbootcliclustercodacolorspacecombinatevdfansifarverfastGHQuadggplot2gluegmpgridExtragtableisobandlabel.switchinglabelingLaplacesDemonlatticelifecyclelme4lpSolvemagrittrMASSMatrixMatrixModelsmclustmcmcMCMCpackmgcvminqamixAKmnormtmunsellmvtnormnlmenloptrnnetpillarpkgconfigquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangRmpfrscalesSparseMsurvivaltibbletruncdistutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Goodness of fit. | BayesT |
| Compute a Bayesian Consensus Clustering model for mixed-type longitudinal data | BCC.multi |
| conRes dataset | conRes |
| epil dataset | epil |
| epil1 model | epil1 |
| epil2 model | epil2 |
| epil3 model | epil3 |
| example model | example |
| example1 model | example1 |
| Model selection | model.selection.criteria |
| PBCseqfit model | PBCseqfit |
| Generic plot method for BCC objects | plot.BCC |
| Generic print method for BCC objects | print.BCC |
| Generic summary method for BCC objects | summary.BCC |
| Trace plot function | traceplot |
| Trajplot for fitted model | trajplot |
