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 |