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 (≥ 0.3.3.9.1), 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 [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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=lgpr to link to this page.
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