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lgpr  

Longitudinal Gaussian Process Regression
View on CRAN: Click here


Download and install lgpr package within the R console
Install from CRAN:
install.packages("lgpr")

Install from Github:
library("remotes")
install_github("cran/lgpr")

Install by package version:
library("remotes")
install_version("lgpr", "1.2.5")



Attach the package and use:
library("lgpr")
Maintained by
Juho Timonen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-06-21
Latest Update: 2023-09-24
Description:
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) .
How to cite:
Juho Timonen (2021). lgpr: Longitudinal Gaussian Process Regression. R package version 1.2.5, https://cran.r-project.org/web/packages/lgpr. Accessed 25 Jun. 2026.
Previous versions and publish date:
1.1.3 (2021-06-21 10:00), 1.1.4 (2021-08-11 07:00), 1.1.5 (2021-09-23 17:00), 1.2.3 (2023-04-06 06:50), 1.2.4 (2023-09-24 08:50)
Other packages that cited lgpr R package
View lgpr citation profile
Other R packages that lgpr depends, imports, suggests or enhances
Complete documentation for lgpr
Functions, R codes and Examples using the lgpr R package
Some associated functions: GaussianPrediction-class . KernelComputer-class . Prediction-class . add_dis_age . add_factor . add_factor_crossing . adjusted_c_hat . apply_scaling . as_character . create_model.covs_and_comps . create_model.formula . create_model.likelihood . create_model . create_model.options . create_model.prior . create_plot_df . create_scaling . dinvgamma_stanlike . draw_pred . example_fit . fit_summary . get_draws . get_pred . kernel . lgp . lgpexpr-class . lgpfit-class . lgpformula-class . lgpmodel-class . lgpr-package . lgprhs-class . lgpscaling-class . lgpsim-class . lgpterm-class . model_summary . new_x . operations . plot_api_c . plot_api_g . plot_components . plot_data . plot_draws . plot_inputwarp . plot_invgamma . plot_pred . plot_sim . ppc . pred . prior_pred . prior_to_num . priors . read_proteomics_data . relevances . s4_generics . sample_model . select . show . sim.create_f . sim.create_x . sim.create_y . sim.kernels . simulate_data . split . testdata_001 . testdata_002 . validate . var_mask . warp_input . 
Some associated R codes: RcppExports.R . aaa.R . main-create_model.R . main-datautil.R . main-draw_pred.R . main-fit_model.R . main-get_pred.R . main-plot_pred.R . main-ppc.R . main-pred.R . main-prior_pred.R . main-relevances.R . main-select.R . methods-KernelComputer.R . methods-Prediction.R . methods-lgpfit.R . methods-lgpformula.R . methods-lgpmodel.R . methods-lgpsim.R . misc-invgamma.R . misc-simulate.R . stanmodels.R . utils-argcheck.R . utils-create_model-covariates.R . utils-create_model-formula.R . utils-create_model-likelihood.R . utils-create_model-prior.R . utils-kernel.R . utils-kernel_reference.R . utils-misc.R . utils-obs_model.R . utils-plot.R . utils-pred.R . zzz.R .  Full lgpr package functions and examples
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