Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models


[Up] [Top]

Documentation for package ‘BayesGP’ version 0.1.3

Help Pages

ccData A simulated dataset from the case-crossover model.
compute_d_step_sgpsd Compute the SD correction factor for sgp
compute_post_fun_iwp Computing the posterior samples of the function or its derivative using the posterior samples of the basis coefficients for iwp
compute_post_fun_sgp Computing the posterior samples of the function using the posterior samples of the basis coefficients for sGP
compute_weights_precision Constructing the precision matrix given the knot sequence
compute_weights_precision_helper Constructing the precision matrix given the knot sequence (helper)
covid_canada The COVID-19 daily death data in Canada.
custom_template Custom Template Function
dummy Roxygen commands
extract_mean_interval_given_samps Construct posterior inference given samples
f Function defined to enhance the usability for users on IDEs.
get_default_option_list_MCMC Get default options for MCMC implementation
global_poly_helper Constructing and evaluating the global polynomials, to account for boundary conditions (design matrix)
global_poly_helper_sgp Constructing and evaluating the global polynomials, to account for boundary conditions (design matrix) of sgp
local_poly_helper Constructing and evaluating the local O-spline basis (design matrix)
model_fit Model fitting with random effects/fixed effects
model_fit_loop Repeated fitting Bayesian Hierarchical Models for a sequence of values of the looping variable.
para_density Obtain the posterior and prior density of all the parameters in the fitted model
PEN_death The monthly all-cause mortality for male with age less than 40 in Pennsylvania.
post_table Obtain the posterior summary table for all the parameters in the fitted model
predict.FitResult To predict the GP component in the fitted model, at the locations specified in 'newdata'.
prior_conversion_iwp Construct prior based on d-step prediction SD (for iwp)
prior_conversion_sgp Construct prior based on d-step prediction SD (for sgp)
sample_fixed_effect Extract the posterior samples from the fitted model for the target fixed variables.
sd_density Obtain the posterior density of a SD parameter in the fitted model
sd_plot Plot the posterior density of a SD parameter in the fitted model