Package index • weave

All functions

Amv(): Observed-system matvec: (S K S^T + diag(noise)) v

M_inv(): Diagonal (Jacobi) preconditioner application

data_complete(): Complete site-time combinations

data_initial_par(): Initialise model parameters

data_missing(): Drop sites with missing data

data_observed_summary(): Calculate observed summary statistics

data_order_index(): Order data and assign identifiers

data_process(): Process raw epidemiological data

fill_vector(): Fill observed values into a full vector

fit(): Fit a spatiotemporal GP to log–counts (matrix-free PCG)

get_spatial_distance(): Pairwise spatial distances

get_temporal_distance(): Pairwise temporal distances

infer_space_kernel_params(): Estimate spatial RBF length scale from empirical correlations

infer_time_kernel_params(): Estimate time-kernel (periodic × RBF) parameters from empirical ACF

kron_mv(): Fast Kronecker–product matrix–vector multiply (times vary fastest)

llh(): Hessian of the (log posterior) for Poisson log-Gaussian model

make_time_folds(): Create contiguous time folds

make_time_folds_interleave(): Create interleaved time folds

pcg(): Preconditioned Conjugate Gradient (PCG) solver for the observed system

periodic_kernel(): Periodic kernel

quick_mvnorm(): Quick multivariate normal samples over two dimensions

rbf_kernel(): Radial basis function kernel

regularise(): Add a small ridge to a square matrix

space_kernel(): Estimate the spatial kernel

time_kernel(): Estimate the temporal kernel

tune_hyperparameters_optim(): Tune hyperparameters by cross-validation