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DGP4LCF: Dependent Gaussian Processes for Longitudinal Correlated Factors

Functionalities for analyzing high-dimensional and longitudinal biomarker data to facilitate precision medicine, using a joint model of Bayesian sparse factor analysis and dependent Gaussian processes. This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) <doi:10.48550/arXiv.2307.02781>.

Version:1.0.0.1
Depends:R (≥ 2.10)
Imports:GPFDA,Rcpp,factor.switching,mvtnorm,combinat,coda,corrplot,pheatmap, stats
LinkingTo:Rcpp,RcppArmadillo
Suggests:knitr,rmarkdown,testthat (≥ 3.0.0)
Published:2025-03-08
DOI:10.32614/CRAN.package.DGP4LCF
Author:Jiachen Cai [aut, cre]
Maintainer:Jiachen Cai <jiachen.cai at mrc-bsu.cam.ac.uk>
License:MIT + fileLICENSE
NeedsCompilation:yes
CRAN checks:DGP4LCF results

Documentation:

Reference manual:DGP4LCF.html ,DGP4LCF.pdf
Vignettes:An Example of Irregular Data Analysis (source,R code)
An Example of Regular Data Analysis (source,R code)

Downloads:

Package source: DGP4LCF_1.0.0.1.tar.gz
Windows binaries: r-devel:DGP4LCF_1.0.0.1.zip, r-release:DGP4LCF_1.0.0.1.zip, r-oldrel:DGP4LCF_1.0.0.1.zip
macOS binaries: r-release (arm64):DGP4LCF_1.0.0.1.tgz, r-oldrel (arm64):DGP4LCF_1.0.0.1.tgz, r-release (x86_64):DGP4LCF_1.0.0.1.tgz, r-oldrel (x86_64):DGP4LCF_1.0.0.1.tgz
Old sources: DGP4LCF archive

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=DGP4LCFto link to this page.


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