Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

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Documentation for package ‘spDBL’ version 1.0.2

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BS Backward sampler for the Forward Filter Backward Sampler (FFBS)
cal_Bt_bt Update the posterior mean and covariance of the discrepancy field
cal_errorbar Compute median and 95% credible interval across rows
cal_errorbar_mean Compute mean and 95% credible interval across rows
cal_jacobian_logit_uniform Log absolute Jacobian of the logit-uniform transformation
check_pds Check and repair a matrix to be positive definite and symmetric
dMTig Log density of the matrix-T distribution with inverse-gamma right covariance
dt_emulation Example emulation dataset
emulator_learn Fit an FFBS-based dynamic emulator
emulator_predict Predict PDE output from a fitted FFBS emulator
expit Logistic (expit) function
FF Forward Filter for the MNIW dynamic linear model
FFBS Forward Filter Backward Sampler (MNIW model)
FFBS_I Forward Filter Backward Sampler (identity right-covariance)
FFBS_predict_exact Exact posterior predictive mean using FFBS smoothed states (MNIW model)
FFBS_predict_MC Monte Carlo prediction using FFBS output (MNIW model)
FFBS_sampling Draw posterior samples from FFBS output (MNIW model)
FFBS_sampling_I Draw posterior samples from FFBS output (identity right-covariance)
FFBS_sampling_sigma2R Draw posterior samples from FFBS output (scalar sigma-squared-times-R model)
FFBS_sigma2R Forward Filter Backward Sampler (scalar sigma-squared-times-R model)
FF_1step_R_I Single forward filter step (identity right-covariance)
FF_1step_R_sigma2R Single forward filter step (scalar right-covariance, sigma-squared times R)
FF_bigdata_R Forward Filter for big data stored in CSV files (MNIW model)
FF_I Forward Filter with identity right-covariance
FF_sigma2R Forward Filter for the scalar-sigma-squared-times-R model
generate.grid.exact Generate an exact block grid analytically
generate.grid.lr Generate a flexible block grid with left-to-right traversal
generate.grid.rowsnake Generate a flexible block grid with snake traversal
generate_grid Generate block indices for big data grid traversal
gen_calibrate_data Generate synthetic calibration data with correlated discrepancy
gen_calibrate_data_uncorr Generate synthetic calibration data with uncorrelated discrepancy
gen_expsq_kernel Compute a squared-exponential (Gaussian) GP kernel matrix
gen_exp_kernel Compute an exponential GP kernel matrix
gen_ffbs_csv Generate synthetic FFBS data and write to CSV files
gen_ffbs_data Generate synthetic FFBS data in memory
gen_F_ls_AR1 Build AR(1) covariate list from a list of response matrices
gen_F_ls_AR1_EP Build AR(1) covariate list for the episode-block model
gen_F_ls_AR2 Build AR(2) covariate list from a list of response matrices
gen_F_ls_AR2_EP Build AR(2) covariate list for the episode-block model
gen_gp_kernel Compute a Gaussian Process covariance kernel matrix
gen_Jt Compute the cross-covariance matrix between observed and new locations
gen_pde Simulate a spatially extended SIR PDE model
gen_pd_matrix Generate a random positive definite matrix
gen_prior_u_tau2 Sample prior discrepancy trajectory and variance sequence
gen_ran_matrix Generate a random matrix with entries scaled to [-1, 1]
inv_chol Invert a matrix via its Cholesky factorisation
lppd_id_1t One-step log posterior predictive density (identity right-covariance model)
lppd_IG_1t One-step log posterior predictive density (scalar sigma-squared-times-R model)
lppd_IW_1t One-step log posterior predictive density (MNIW / inverse-Wishart model)
make_pds Force a matrix to be positive definite and symmetric
MNIG_sampler Sample from the Matrix Normal Inverse Gamma (MNIG) distribution
MNIW_R MNIW posterior update
MNIW_R_naiive Naive MNIW posterior update
MNIW_sampler Sample from the Matrix Normal Inverse Wishart (MNIW) distribution
plot_panel_heatmap_9 Plot a 3-by-3 panel of heatmaps across selected time stamps
plot_panel_heatmap_9_cal Plot a 3-by-3 panel of calibration heatmaps
plot_panel_heatmap_9_cal_nolab Plot a 3-by-3 panel of calibration heatmaps without axis labels
prepare_data Prepare PDE emulator training and testing data from CSV files
quick_heat Quick raster heatmap
quick_save Save a ggplot to a timestamped PNG file
read_big_csv_quick Read a rectangular block from a large CSV file
recover_from_EP_exact Recover episode-partitioned data to original time dimension (exact)
recover_from_EP_MC Recover episode-partitioned posterior samples to original time dimension
rmn_chol Draw one sample from a matrix-normal distribution (Cholesky parameterisation)
rmn_chol_more Draw multiple samples from a matrix-normal distribution (Cholesky parameterisation)
sample_y_eta_one Draw predictive samples from a precomputed mean and covariance
scale_back_uniform Invert a uniform scaling transformation
scale_uniform Scale a vector to the unit interval via a uniform transformation
SIR Right-hand side of the spatially extended SIR ODE
update_muSigma_eta_one Compute posterior predictive mean and covariance without sampling (single sample)
update_y_eta Update the likelihood of observations given PDE parameters (Monte Carlo)
update_y_eta_one Update the likelihood of observations given PDE parameters (single sample)