R: Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

# Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

## Documentation for package ‘spDBL’ version 1.0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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)