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
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: 2024-05-28
DOI: 10.32614/CRAN.package.DGP4LCF
Author: Jiachen Cai [aut, cre]
Maintainer: Jiachen Cai <jiachen.cai at mrc-bsu.cam.ac.uk>
License: MIT + file LICENSE
NeedsCompilation: yes
CRAN checks: DGP4LCF results


Reference manual: DGP4LCF.pdf
Vignettes: An Example of Irregular Data Analysis
An Example of Regular Data Analysis


Package source: DGP4LCF_1.0.0.tar.gz
Windows binaries: r-devel: DGP4LCF_1.0.0.zip, r-release: DGP4LCF_1.0.0.zip, r-oldrel: DGP4LCF_1.0.0.zip
macOS binaries: r-release (arm64): DGP4LCF_1.0.0.tgz, r-oldrel (arm64): DGP4LCF_1.0.0.tgz, r-release (x86_64): DGP4LCF_1.0.0.tgz, r-oldrel (x86_64): DGP4LCF_1.0.0.tgz


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