Package index

All functions

BPS_PseudoBMA()

Combine subset models wiht Pseudo-BMA

BPS_combine()

Combine subset models wiht BPS

BPS_post_MvT()

Perform the BPS sampling from posterior and posterior predictive given a set of stacking weights

BPS_postdraws_MvT()

Compute the BPS posterior samples given a set of stacking weights

BPS_pred_MvT()

Compute the BPS spatial prediction given a set of stacking weights

BPS_weights_MvT()

Compute the BPS weights by convex optimization

CVXR_opt()

Compute the BPS weights by convex optimization

arma_dist()

Compute the Euclidean distance matrix

bayesMvLMconjugate()

Gibbs sampler for Conjugate Bayesian Multivariate Linear Models

conv_opt()

Solver for Bayesian Predictive Stacking of Predictive densities convex optimization problem

d_pred_cpp_MvT()

Evaluate the density of a set of unobserved response with respect to the conditional posterior predictive

dens_kcv_MvT()

Compute the KCV of the density evaluations for fixed values of the hyperparameters

dens_loocv_MvT()

Compute the LOOCV of the density evaluations for fixed values of the hyperparameters

expand_grid_cpp()

Build a grid from two vector (i.e. equivalent to expand.grid() in R)

fit_cpp_MvT()

Compute the parameters for the posteriors distribution of \(\beta\) and \(\Sigma\) (i.e. updated parameters)

forceSymmetry_cpp()

Function to subset data for meta-analysis

models_dens_MvT()

Return the CV predictive density evaluations for all the model combinations

post_draws_MvT()

Sample R draws from the posterior distributions

pred_bayesMvLMconjugate()

Predictive sampler for Conjugate Bayesian Multivariate Linear Models

r_pred_cond_MvT()

Draw from the conditional posterior predictive for a set of unobserved covariates

r_pred_joint_MvT()

Draw from the joint posterior predictive for a set of unobserved covariates

r_pred_marg_MvT()

Draw from the joint posterior predictive for a set of unobserved covariates

sample_index()

Function to sample integers (index)

spBPS()

Unified spatial BPS workflow (multivariate path, works for q = 1)

subset_data()

Function to subset data for meta-analysis