R: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models

# Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models

## Documentation for package ‘BayesGP’ version 0.1.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## 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