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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
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

Documentation:

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

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=DGP4LCF to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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