Package index • weave

All functions

Amv(): Observed-system matvec: (S K S^T + diag(noise)) v
build_plugin_field(): Build a plug-in latent field from observed counts
data_complete(): Complete site-time combinations
data_missing(): Drop sites with missing data
data_order_index(): Order data and assign identifiers
data_process(): Process raw epidemiological data for the GP model
default_kernel_priors(): Default priors for the kernel hyperparameters
fill_vector(): Fill observed values into a full vector
get_spatial_distance(): Pairwise spatial distances
get_temporal_distance(): Pairwise temporal distances
gp_marginal_loglik(): Exact separable-GP marginal log-likelihood of a field, with a nugget
gp_predict(): Predict the latent rate and a count prediction interval (PCG)
infer_kernel_params(): Quick exact-marginal-likelihood estimate of the kernel hyperparameters
kdiag_from_factors(): Kronecker diagonal of a separable kernel
kron_mv(): Fast Kronecker–product matrix–vector multiply (times vary fastest)
pcg(): Preconditioned Conjugate Gradient (PCG) solver for the observed system
periodic_kernel(): Periodic kernel
quick_mvnorm(): Quick multivariate normal samples over two dimensions
quick_mvnorm_chol(): Quick multivariate normal samples over two dimensions (cholesky precomputed)
rbf_kernel(): Radial basis function kernel
space_kernel(): Estimate the spatial kernel
time_kernel(): Estimate the temporal kernel