R-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.
See overview, tutorials, vignettes and documentation at https://jtimonen.github.io/lgpr-usage/index.html.
- The package should work on all major operating systems.
- R 3.4 or later is required, R 4.2 or later is recommended
- The latest released version that is available from CRAN can be installed simply via
install.packages("lgpr")Installing from CRAN is probably the easiest option since they might have binaries for your system (so no need to build the package from source yourself).
- The latest released version (which might not be in CRAN yet) can be installed via
install.packages('devtools') # if you don't have devtools already
devtools::install_github('jtimonen/lgpr', build_vignettes = TRUE)- The latest development version can be installed via
devtools::install_github('jtimonen/lgpr', ref = "develop")Github installations are source installations (they require a C++ compiler).
- If you have trouble installing the dependency rstan, see these instructions
- Installing from source requires that you have your toolchain setup properly. See the instructions for:
If you are using R version 4.1 or earlier, you can get an error
cc1plus.exe: out of memory allocating 65536 bytes
make: *** [C:/PROGRA~1/R/R-40~1.2/etc/i386/Makeconf:227: stanExports_lgp_latent.o] Error 1
because both 64-bit and 32-bit versions of the package are getting installed. To disable this and resolve error,
ugrade to latest R or install the version that has Biarch: false by
devtools::install_github('jtimonen/lgpr', ref = "no-biarch")For code to reproduce the experiments of our manuscript see https://github.com/jtimonen/lgpr-usage. Preprocessed longitudinal proteomics
data is also provided there. See also the built-in read_proteomics_data() function.