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Package: lgpr 1.2.5

Juho Timonen

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>.

Authors:Juho Timonen [aut, cre], Andrew Johnson [ctb]

lgpr_1.2.5.tar.gz
lgpr_1.2.5.zip(r-4.7-x86_64)lgpr_1.2.5.zip(r-4.7-arm64)lgpr_1.2.5.zip(r-4.6-x86_64)lgpr_1.2.5.zip(r-4.6-arm64)
lgpr_1.2.5.tar.gz(r-4.7-arm64)lgpr_1.2.5.tar.gz(r-4.7-x86_64)lgpr_1.2.5.tar.gz(r-4.6-arm64)lgpr_1.2.5.tar.gz(r-4.6-x86_64)
lgpr_1.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
lgpr/json (API)

# Install 'lgpr' in R:

install.packages('lgpr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jtimonen/lgpr/issues

Uses libs:

onetbb– Parallelism library for C++
c++– GNU Standard C++ Library v3

Datasets:

testdata_001 - A very small artificial test data, used mostly for unit tests
testdata_002 - Medium-size artificial test data, used mostly for tutorials

On CRAN:

onetbb cpp

3.70 score 100 scripts 349 downloads 62 exports 63 dependencies

Last updated from:00c84cd224. Checks:10 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	373
linux-devel-x86_64	OK	336
source / vignettes	OK	640
linux-release-arm64	OK	372
linux-release-x86_64	OK	459
windows-devel-arm64	OK	501
windows-devel-x86_64	OK	487
windows-release-arm64	OK	478
windows-release-x86_64	OK	529
wasm-release	OK	247

Exports:add_dis_age add_factor add_factor_crossing adjusted_c_hat bet clear_postproc component_info component_names contains_postproc covariate_info create_model draw_pred fit_summary gam get_draws get_model get_pred get_stanfit igam is_f_sampled lgp log_normal model_summary new_x normal num_components num_evalpoints num_paramsets optimize_model param_summary parameter_info plot plot_beta plot_components plot_data plot_draws plot_effect_times plot_f plot_pred plot_sim plot_warp postproc ppc pred prior_pred read_proteomics_data relevances sample_model sample_param_prior select select_freq select_freq.integrate select.integrate show simulate_data split_by_factor split_data split_random split_within_factor split_within_factor_random student_t uniform

Dependencies:abind backports bayesplot BH bitops callr checkmate cli cpp11 desc distributional dplyr farver generics ggdist ggplot2 ggridges glue gridExtra gtable inline isoband labeling lifecycle loo magrittr MASS matrixStats numDeriv otel pillar pkgbuild pkgconfig plyr posterior processx ps purrr quadprog QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel RCurl reshape2 rlang rstan rstantools S7 scales StanHeaders stringi stringr tensorA tibble tidyr tidyselect utf8 vctrs viridisLite withr

Mathematical description of lgpr models

Rendered frommath.Rmdusingknitr::rmarkdown

Last update: 2021-08-11
Started: 2021-08-11

Help page	Topics
The 'lgpr' package.	lgpr-package lgpr
Easily add the disease-related age variable to a data frame	add_dis_age
Easily add a categorical covariate to a data frame	add_factor
Add a crossing of two factors to a data frame	add_factor_crossing
Set the GP mean vector, taking TMM or other normalization into account	adjusted_c_hat
Apply variable scaling	apply_scaling
Character representations of different formula objects	as.character,lgpexpr-method as.character,lgpformula-method as.character,lgpterm-method as_character
Create a model	create_model
Parse the covariates and model components from given data and formula	create_model.covs_and_comps
Create a model formula	create_model.formula
Parse the response variable and its likelihood model	create_model.likelihood
Parse the given modeling options	create_model.options
Parse given prior	create_model.prior
Helper function for plots	create_plot_df
Create a standardizing transform	create_scaling
Density and quantile functions of the inverse gamma distribution	dinvgamma_stanlike qinvgamma_stanlike
Draw pseudo-observations from posterior or prior predictive distribution	draw_pred
Quick way to create an example lgpfit, useful for debugging	example_fit
Print a fit summary.	fit_summary
An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model	component_names,GaussianPrediction-method GaussianPrediction GaussianPrediction-class num_components,GaussianPrediction-method num_evalpoints,GaussianPrediction-method num_paramsets,GaussianPrediction-method show,GaussianPrediction-method
Extract parameter draws from lgpfit or stanfit	get_draws
Extract model predictions and function posteriors	get_pred
Compute a kernel matrix (covariance matrix)	kernel kernel_beta kernel_bin kernel_cat kernel_eq kernel_ns kernel_varmask kernel_zerosum
An S4 class to represent input for kernel matrix computations	component_names,KernelComputer-method KernelComputer KernelComputer-class num_components,KernelComputer-method num_evalpoints,KernelComputer-method num_paramsets,KernelComputer-method show,KernelComputer-method
Main function of the 'lgpr' package	lgp
An S4 class to represent an lgp expression	lgpexpr lgpexpr-class
An S4 class to represent the output of the 'lgp' function	clear_postproc,lgpfit-method component_names,lgpfit-method contains_postproc,lgpfit-method get_model,lgpfit-method get_stanfit,lgpfit-method is_f_sampled,lgpfit-method lgpfit lgpfit-class num_components,lgpfit-method plot,lgpfit,missing-method postproc,lgpfit-method show,lgpfit-method
An S4 class to represent an lgp formula	lgpformula lgpformula-class
An S4 class to represent an additive GP model	component_info,lgpmodel-method component_names,lgpmodel-method covariate_info,lgpmodel-method is_f_sampled,lgpmodel-method lgpmodel lgpmodel-class num_components,lgpmodel-method parameter_info,lgpmodel-method show,lgpmodel-method
An S4 class to represent the right-hand side of an lgp formula	lgprhs lgprhs-class
An S4 class to represent variable scaling	lgpscaling lgpscaling-class
An S4 class to represent a data set simulated using the additive GP formalism	lgpsim lgpsim-class plot,lgpsim,missing-method show,lgpsim-method
An S4 class to represent one formula term	lgpterm lgpterm-class
Print a model summary.	model_summary param_summary
Create test input points for prediction	new_x
Operations on formula terms and expressions	*,lgpterm,lgpterm-method +,lgprhs,lgprhs-method +,lgprhs,lgpterm-method +,lgpterm,lgpterm-method operations
Plot a generated/fit model component	plot_api_c
Plot longitudinal data and/or model fit so that each subject/group has their own panel	plot_api_g
Visualize all model components	plot_components
Vizualizing longitudinal data	plot_data
Visualize the distribution of parameter draws	plot_beta plot_draws plot_effect_times plot_warp
Visualize input warping function with several steepness parameter values	plot_inputwarp
Plot the inverse gamma-distribution pdf	plot_invgamma
Visualizing model predictions or inferred covariate effects	plot_f plot_pred
Visualize an lgpsim object (simulated data)	plot_sim
Graphical posterior predictive checks	ppc
Posterior predictions and function posteriors	pred
An S4 class to represent prior or posterior draws from an additive function distribution.	component_names,Prediction-method num_components,Prediction-method num_evalpoints,Prediction-method num_paramsets,Prediction-method Prediction Prediction-class show,Prediction-method
Prior (predictive) sampling	prior_pred sample_param_prior
Convert given prior to numeric format	prior_to_num
Prior definitions	bet gam gam, igam igam, log_normal log_normal, normal normal, priors student_t student_t, uniform uniform,
Function for reading the built-in proteomics data	read_proteomics_data
Assess component relevances	relevances
S4 generics for lgpfit, lgpmodel, and other objects	clear_postproc component_info component_names contains_postproc covariate_info get_model get_stanfit is_f_sampled num_components num_evalpoints num_paramsets parameter_info postproc s4_generics
Fitting a model	optimize_model sample_model
Select relevant components	select select.integrate select_freq select_freq.integrate
Printing formula object info using the show generic	show show,lgpformula-method show,lgprhs-method show,lgpterm-method
Simulate latent function components for longitudinal data analysis	sim.create_f
Create an input data frame X for simulated data	sim.create_x
Simulate noisy observations	sim.create_y
Compute all kernel matrices when simulating data	sim.kernels
Generate an artificial longitudinal data set	simulate_data
Split data into training and test sets	split split_by_factor split_data split_random split_within_factor split_within_factor_random
A very small artificial test data, used mostly for unit tests	testdata_001
Medium-size artificial test data, used mostly for tutorials	testdata_002
Validate S4 class objects	validate validate_GaussianPrediction validate_lgpexpr validate_lgpfit validate_lgpformula validate_lgpscaling validate_Prediction
Variance masking function	var_mask
Input warping function	warp_input

Package: lgpr 1.2.5

lgpr: Longitudinal Gaussian Process Regression

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)