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) .


Reference manual

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1.1.5 by Juho Timonen, a month ago


Report a bug at https://github.com/jtimonen/lgpr/issues

Browse source code at https://github.com/cran/lgpr

Authors: Juho Timonen [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Rcpp, RcppParallel, RCurl, rstan, rstantools, bayesplot, MASS, stats, ggplot2, gridExtra

Depends on methods

Suggests knitr, rmarkdown, testthat, covr

Linking to BH, Rcpp, RcppEigen, RcppParallel, rstan, StanHeaders

System requirements: GNU make

See at CRAN