lgpr: Longitudinal Gaussian Process Regression

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Version: 1.2.4
Depends: R (≥ 3.4.0), methods
Imports: Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.2), RCurl (≥ 1.98), rstan (≥ 2.26.0), rstantools (≥ 2.3.1), bayesplot (≥ 1.7.0), MASS (≥ 7.3-50), stats (≥ 3.4), ggplot2 (≥ 3.1.0), gridExtra (≥ 0.3.0)
LinkingTo: BH (≥ 1.75.0-0), Rcpp (≥ 1.0.6), RcppEigen (≥, RcppParallel (≥ 5.0.2), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0)
Suggests: knitr, rmarkdown, testthat, covr
Published: 2023-09-24
DOI: 10.32614/CRAN.package.lgpr
Author: Juho Timonen ORCID iD [aut, cre], Andrew Johnson [ctb]
Maintainer: Juho Timonen <juho.timonen at iki.fi>
BugReports: https://github.com/jtimonen/lgpr/issues
License: GPL (≥ 3)
URL: https://github.com/jtimonen/lgpr
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: lgpr citation info
Materials: README
CRAN checks: lgpr results


Reference manual: lgpr.pdf
Vignettes: Mathematical description of lgpr models


Package source: lgpr_1.2.4.tar.gz
Windows binaries: r-devel: lgpr_1.2.4.zip, r-release: lgpr_1.2.4.zip, r-oldrel: lgpr_1.2.4.zip
macOS binaries: r-release (arm64): lgpr_1.2.4.tgz, r-oldrel (arm64): lgpr_1.2.4.tgz, r-release (x86_64): lgpr_1.2.4.tgz, r-oldrel (x86_64): lgpr_1.2.4.tgz
Old sources: lgpr archive


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