Package: BayesGP 0.1.3

Ziang Zhang

BayesGP: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models

Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.

Authors:Ziang Zhang [aut, cre], Yongwei Lin [aut], Alex Stringer [aut], Patrick Brown [aut]

BayesGP_0.1.3.tar.gz
BayesGP_0.1.3.zip(r-4.7-x86_64)BayesGP_0.1.3.zip(r-4.7-arm64)BayesGP_0.1.3.zip(r-4.6-x86_64)BayesGP_0.1.3.zip(r-4.6-arm64)BayesGP_0.1.3.zip(r-4.5-x86_64)
BayesGP_0.1.3.tgz(r-4.6-x86_64)BayesGP_0.1.3.tgz(r-4.6-arm64)BayesGP_0.1.3.tgz(r-4.5-x86_64)BayesGP_0.1.3.tgz(r-4.5-arm64)
BayesGP_0.1.3.tar.gz(r-4.7-arm64)BayesGP_0.1.3.tar.gz(r-4.7-x86_64)BayesGP_0.1.3.tar.gz(r-4.6-arm64)BayesGP_0.1.3.tar.gz(r-4.6-x86_64)
BayesGP_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BayesGP/json (API)

# Install 'BayesGP' in R:
install.packages('BayesGP', repos = c('https://bayes-gp.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bayes-gp/bayesgp/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • ccData - A simulated dataset from the case-crossover model.
  • covid_canada - The COVID-19 daily death data in Canada.
  • PEN_death - The monthly all-cause mortality for male with age less than 40 in Pennsylvania.

On CRAN:

Conda:

cpp

5.77 score 4 stars 98 scripts 489 downloads 21 exports 81 dependencies

Last updated from:7db1d65825. Checks:13 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE179
linux-devel-x86_64NOTE218
source / vignettesOK367
linux-release-arm64NOTE210
linux-release-x86_64NOTE218
macos-release-arm64NOTE227
macos-release-x86_64NOTE367
macos-oldrel-arm64NOTE274
macos-oldrel-x86_64NOTE390
windows-devel-arm64NOTE462
windows-devel-x86_64NOTE249
windows-release-arm64NOTE377
windows-release-x86_64NOTE173
windows-oldrel-x86_64NOTE207
wasm-releaseOK151

Exports:compute_post_fun_iwpcompute_post_fun_sgpcompute_weights_precision_helpercustom_templateextract_mean_interval_given_sampsfget_default_option_list_MCMCglobal_poly_helperglobal_poly_helper_sgplocal_poly_helpermodel_fitmodel_fit_looppara_densitypara_sampspost_tableprior_conversion_iwpprior_conversion_sgpsample_fixed_effectsample_sdsd_densitysd_plot

Dependencies:abindaghqashbackportsBHbitopscallrcheckmatecliclustercolorspacecpp11data.tabledescdeSolvedistributionalfarverfdafdsFNNgenericsggplot2gluegridExtragtablehdrcdeinlineisobandkernlabKernSmoothkslabelingLaplacesDemonlatticelifecyclelocfitloomagrittrMASSMatrixmatrixStatsmclustmgcvmulticoolmvQuadmvtnormnlmenumDerivotelpcaPPpillarpkgbuildpkgconfigpolynomposteriorpracmaprocessxpsQuickJSRR6rainbowRColorBrewerRcppRcppEigenRcppParallelRCurlrlangrstanS7scalessfsmiscStanHeadersstatmodtensorAtibbleTMBtmbstanutf8vctrsviridisLitewithr

BayesGP: COVID-19 Example
COVID-19 Example | Data and Model | Inference
Rendered fromBayesGP-covid_example.Rmdusingknitr::rmarkdown

Last update: 2024-12-02
Started: 2023-10-12

BayesGP: Fitting sGP
Fitting sGP | Prior Elicitation: | Model Fitting:
Rendered fromBayesGP-sGP.Rmdusingknitr::rmarkdown

Last update: 2024-12-02
Started: 2023-10-19

BayesGP: Fitting Model with Partial Likelihood
Partial Likelihood Models in BayesGP | Case-Crossover Model | CoxPH Model
Rendered fromBayesGP-Partial_Likelihood.Rmdusingknitr::rmarkdown

Last update: 2024-12-02
Started: 2023-10-19

Readme and manuals

Help Manual

Help pageTopics
A simulated dataset from the case-crossover model.ccData
Compute the SD correction factor for sgpcompute_d_step_sgpsd
Computing the posterior samples of the function or its derivative using the posterior samples of the basis coefficients for iwpcompute_post_fun_iwp
Computing the posterior samples of the function using the posterior samples of the basis coefficients for sGPcompute_post_fun_sgp
Constructing the precision matrix given the knot sequencecompute_weights_precision
Constructing the precision matrix given the knot sequence (helper)compute_weights_precision_helper
The COVID-19 daily death data in Canada.covid_canada
Custom Template Functioncustom_template
Roxygen commandsdummy
Construct posterior inference given samplesextract_mean_interval_given_samps
Function defined to enhance the usability for users on IDEs.f
Get default options for MCMC implementationget_default_option_list_MCMC
Constructing and evaluating the global polynomials, to account for boundary conditions (design matrix)global_poly_helper
Constructing and evaluating the global polynomials, to account for boundary conditions (design matrix) of sgpglobal_poly_helper_sgp
Constructing and evaluating the local O-spline basis (design matrix)local_poly_helper
Model fitting with random effects/fixed effectsmodel_fit
Repeated fitting Bayesian Hierarchical Models for a sequence of values of the looping variable.model_fit_loop
Obtain the posterior and prior density of all the parameters in the fitted modelpara_density
Extract the posterior samples from the fitted model for the target parameters.para_samps
The monthly all-cause mortality for male with age less than 40 in Pennsylvania.PEN_death
Obtain the posterior summary table for all the parameters in the fitted modelpost_table
To predict the GP component in the fitted model, at the locations specified in `newdata`.predict.FitResult
Construct prior based on d-step prediction SD (for iwp)prior_conversion_iwp
Construct prior based on d-step prediction SD (for sgp)prior_conversion_sgp
Extract the posterior samples from the fitted model for the target fixed variables.sample_fixed_effect
Extract the posterior samples from the fitted model for the target standard deviation.sample_sd
Obtain the posterior density of a SD parameter in the fitted modelsd_density
Plot the posterior density of a SD parameter in the fitted modelsd_plot