BS                      Backward sampler for the Forward Filter
                        Backward Sampler (FFBS)
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_MC         Monte Carlo prediction using FFBS output (MNIW
                        model)
FFBS_predict_exact      Exact posterior predictive mean using FFBS
                        smoothed states (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_I                    Forward Filter with identity right-covariance
FF_bigdata_R            Forward Filter for big data stored in CSV files
                        (MNIW model)
FF_sigma2R              Forward Filter for the
                        scalar-sigma-squared-times-R model
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
SIR                     Right-hand side of the spatially extended SIR
                        ODE
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
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_Jt                  Compute the cross-covariance matrix between
                        observed and new locations
gen_calibrate_data      Generate synthetic calibration data with
                        correlated discrepancy
gen_calibrate_data_uncorr
                        Generate synthetic calibration data with
                        uncorrelated discrepancy
gen_exp_kernel          Compute an exponential GP kernel matrix
gen_expsq_kernel        Compute a squared-exponential (Gaussian) 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_gp_kernel           Compute a Gaussian Process covariance kernel
                        matrix
gen_pd_matrix           Generate a random positive definite matrix
gen_pde                 Simulate a spatially extended SIR PDE model
gen_prior_u_tau2        Sample prior discrepancy trajectory and
                        variance sequence
gen_ran_matrix          Generate a random matrix with entries scaled to
                        [-1, 1]
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
inv_chol                Invert a matrix via its Cholesky factorisation
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)
lppd_id_1t              One-step log posterior predictive density
                        (identity right-covariance model)
make_pds                Force a matrix to be positive definite and
                        symmetric
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_MC      Recover episode-partitioned posterior samples
                        to original time dimension
recover_from_EP_exact   Recover episode-partitioned data to original
                        time dimension (exact)
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
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)
