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 (≥ 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
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:

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

Downloads:

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

Linking:

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