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lgpr: Longitudinal Gaussian Process Regression (original) (raw)

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: 1.2.5
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 (≥ 0.3.3.9.1), RcppParallel (≥ 5.0.2), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0)
Suggests: knitr, rmarkdown, testthat, covr
Published: 2025-10-30
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

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