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.tar.gz(r-4.5-noble)BayesGP_0.1.3.tar.gz(r-4.4-noble)
BayesGP_0.1.3.tgz(r-4.4-emscripten)
BayesGP.pdf |BayesGP.html✨
BayesGP/json (API)
NEWS

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

Peer review:

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

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

3.18 score 71 scripts 489 downloads 19 exports 80 dependencies

Last updated 1 months agofrom:7da6f52713. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 13 2024
R-4.5-linux-x86_64OKDec 13 2024

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_densitypost_tableprior_conversion_iwpprior_conversion_sgpsample_fixed_effectsd_densitysd_plot

Dependencies:abindaghqashbackportsBHbitopscallrcheckmatecliclustercolorspacedata.tabledescdeSolvedistributionalfansifarverfdafdsFNNgenericsggplot2gluegridExtragtablehdrcdeinlineisobandkernlabKernSmoothkslabelingLaplacesDemonlatticelifecyclelocfitloomagrittrMASSMatrixmatrixStatsmclustmgcvmulticoolmunsellmvQuadmvtnormnlmenumDerivpcaPPpillarpkgbuildpkgconfigpolynomposteriorpracmaprocessxpsQuickJSRR6rainbowRColorBrewerRcppRcppEigenRcppParallelRCurlrlangrstanscalessfsmiscStanHeadersstatmodtensorAtibbleTMBtmbstanutf8vctrsviridisLitewithr

BayesGP: COVID-19 Example

Rendered fromBayesGP-covid_example.Rmdusingknitr::rmarkdownon Dec 13 2024.

Last update: 2024-11-12
Started: 2024-11-12

BayesGP: Fitting sGP

Rendered fromBayesGP-sGP.Rmdusingknitr::rmarkdownon Dec 13 2024.

Last update: 2024-11-12
Started: 2024-11-12

BayesGP: Fitting Model with Partial Likelihood

Rendered fromBayesGP-Partial_Likelihood.Rmdusingknitr::rmarkdownon Dec 13 2024.

Last update: 2024-11-12
Started: 2024-11-12

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
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
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