Bayesian Surprise for De-Biasing Thematic Maps R package bayesiansurpriser version 0.1.0 Implements Bayesian Surprise methodology for data visualization, based on Correll and Heer (2017) <doi:10.1109/TVCG.2016.2598839> "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides tools to weight event data relative to spatio-temporal models, highlighting unexpected patterns while de-biasing against known factors like population density or sampling variation. Integrates seamlessly with 'sf' for spatial data and 'ggplot2' for visualization. Supports temporal/streaming data analysis. Software Depends: R (>= 4.1.0) Imports: ggplot2 (>= 3.5.0), sf (>= 1.0.0), scales (>= 1.3.0), rlang (>= 1.1.0), cli, stats, MASS, RColorBrewer Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tibble, vdiffr, tidycensus, tigris, cancensus, ggrepel Dmitry Shkolnik <shkolnikd@gmail.com> Comprehensive R Archive Network (CRAN) Dmitry Shkolnik [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=bayesiansurpriser/LICENSE) 2026-04-21 application/tgz https://CRAN.R-project.org/package=bayesiansurpriser doi:10.32614/CRAN.package.bayesiansurpriser