bayesiansurpriser
Bayesian Surprise for De-Biasing Thematic Maps
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | bayesiansurpriser_0.1.0.tar.gz |
2.3 MiB |
0.1.0 |
rolling linux/noble R-4.5 | bayesiansurpriser_0.1.0.tar.gz |
2.3 MiB |
0.1.0 |
rolling source/ R- | bayesiansurpriser_0.1.0.tar.gz |
2.1 MiB |
0.1.0 |
latest linux/jammy R-4.5 | bayesiansurpriser_0.1.0.tar.gz |
2.3 MiB |
0.1.0 |
latest linux/noble R-4.5 | bayesiansurpriser_0.1.0.tar.gz |
2.3 MiB |
0.1.0 |
latest source/ R- | bayesiansurpriser_0.1.0.tar.gz |
2.1 MiB |
0.1.0 |
2026-04-26 source/ R- | bayesiansurpriser_0.1.0.tar.gz |
2.1 MiB |
0.1.0 |
2026-04-23 source/ R- | bayesiansurpriser_0.1.0.tar.gz |
2.1 MiB |