Package: spDBL 1.0.2

Xiang Chen

spDBL: Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

Provides tools for Bayesian learning of spatiotemporal dynamical mechanistic models. Includes methods for parameter estimation, simulation, and inference using hierarchical and state-space modeling approaches, following Banerjee, Chen, Frankenburg and Zhou (2025) <https://jmlr.org/papers/v26/22-0896.html>.

Authors:Xiang Chen [aut, cre], Sudipto Banerjee [aut]

spDBL_1.0.2.tar.gz
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spDBL_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
spDBL/json (API)

# Install 'spDBL' in R:

install.packages('spDBL', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

dt_emulation - Example emulation dataset

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.70 score 288 downloads 71 exports 96 dependencies

Last updated from:ce91035c80. Checks:10 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	188
linux-devel-x86_64	OK	248
source / vignettes	OK	359
linux-release-arm64	OK	161
linux-release-x86_64	OK	197
windows-devel-arm64	OK	218
windows-devel-x86_64	OK	162
windows-release-arm64	OK	257
windows-release-x86_64	OK	165
wasm-release	OK	181

Exports:BS cal_Bt_bt cal_errorbar cal_errorbar_mean cal_jacobian_logit_uniform check_pds dMTig emulator_learn emulator_predict expit FF FF_1step_R_I FF_1step_R_sigma2R FF_bigdata_R FF_I FF_sigma2R FFBS FFBS_I FFBS_predict_exact FFBS_predict_MC FFBS_sampling FFBS_sampling_I FFBS_sampling_sigma2R FFBS_sigma2R gen_calibrate_data gen_calibrate_data_uncorr gen_exp_kernel gen_expsq_kernel gen_F_ls_AR1 gen_F_ls_AR1_EP gen_F_ls_AR2 gen_F_ls_AR2_EP gen_ffbs_csv gen_ffbs_data gen_gp_kernel gen_Jt gen_pd_matrix gen_pde gen_prior_u_tau2 gen_ran_matrix generate_grid generate.grid.exact generate.grid.lr generate.grid.rowsnake inv_chol lppd_id_1t lppd_IG_1t lppd_IW_1t make_pds MNIG_sampler MNIW_R MNIW_R_naiive MNIW_sampler plot_panel_heatmap_9 plot_panel_heatmap_9_cal plot_panel_heatmap_9_cal_nolab prepare_data quick_heat quick_save read_big_csv_quick recover_from_EP_exact recover_from_EP_MC rmn_chol rmn_chol_more sample_y_eta_one scale_back_uniform scale_uniform SIR update_muSigma_eta_one update_y_eta update_y_eta_one

Dependencies:abind backports bit bit64 boot broom car carData cli clipr colorspace corrplot cowplot cpp11 crayon Deriv deSolve doBy dplyr farver forecast Formula fracdiff generics ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable hms invgamma isoband keep labeling LaplacesDemon lattice lifecycle lme4 lmtest magrittr MASS Matrix matrixcalc MatrixModels matrixsampling mgcv minqa mniw modelr nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig polynom prettyunits progress purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack ReacTran readr reformulas rlang rootSolve rstatix S7 scales shape SparseM stringi stringr survival tibble tidyr tidyselect timeDate tzdb urca utf8 vctrs viridisLite vroom withr zoo

PDE Emulation with FFBS

Rendered fromemulation.Rmdusingknitr::rmarkdown

Last update: 2026-06-09
Started: 2026-06-09

Citation

Readme and manuals

Help Manual

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

Package: spDBL 1.0.2

spDBL: Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)