Longitudinal Gaussian Process Regression R package lgpr version 1.2.5 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>. Software 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 Juho Timonen <juho.timonen@iki.fi> Comprehensive R Archive Network (CRAN) Juho Timonen [aut, cre] (ORCID: <https://orcid.org/0000-0003-2341-6765>), Andrew Johnson [ctb] GPL (>= 3) 2025-10-30 application/tgz https://CRAN.R-project.org/package=lgpr doi:10.32614/CRAN.package.lgpr