Package: bayesiansurpriser 0.1.0

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.

Authors:Dmitry Shkolnik [aut, cre]

bayesiansurpriser_0.1.0.tar.gz
bayesiansurpriser_0.1.0.zip(r-4.7)bayesiansurpriser_0.1.0.zip(r-4.6)bayesiansurpriser_0.1.0.zip(r-4.5)
bayesiansurpriser_0.1.0.tgz(r-4.6-any)bayesiansurpriser_0.1.0.tgz(r-4.5-any)
bayesiansurpriser_0.1.0.tar.gz(r-4.7-any)bayesiansurpriser_0.1.0.tar.gz(r-4.6-any)
bayesiansurpriser_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bayesiansurpriser/json (API)
NEWS

# Install 'bayesiansurpriser' in R:
install.packages('bayesiansurpriser', repos = c('https://dshkol.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dshkol/bayesiansurpriser/issues

Pkgdown/docs site:https://dshkol.github.io

Datasets:

On CRAN:

Conda:

5.30 score 570 downloads 63 exports 29 dependencies

Last updated from:83e5f8b06e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK171
source / vignettesOK220
linux-release-x86_64OK178
macos-release-arm64OK157
macos-oldrel-arm64OK196
windows-develOK144
windows-releaseOK103
windows-oldrelOK118
wasm-releaseOK132

Exports:add_modelauto_surprisebayesian_updatebs_model_baseratebs_model_baserate_colbs_model_bootstrapbs_model_funnelbs_model_funnel_colbs_model_gaussianbs_model_gaussian_mixturebs_model_sampledbs_model_uniformcompute_funnel_datacompute_surprisecumulative_bayesian_updatedefault_model_spacefunnel_pvaluefunnel_zscoregeom_surprisegeom_surprise_densitygeom_surprise_histogramget_model_spaceget_surpriseget_surprise_at_timekl_divergencelog_sum_expmodel_namesmodel_spacen_modelsnormalize_minmaxnormalize_probnormalize_ratenormalize_robustnormalize_zscoreremove_modelscale_color_surprisescale_color_surprise_binnedscale_color_surprise_divergingscale_color_surprise_thresholdsscale_colour_surprisescale_colour_surprise_binnedscale_colour_surprise_divergingscale_colour_surprise_thresholdsscale_fill_surprisescale_fill_surprise_binnedscale_fill_surprise_divergingscale_fill_surprise_diverging_binnedscale_fill_surprise_manualscale_fill_surprise_thresholdsset_priorst_aggregate_surprisest_densityst_density_atst_surprisestat_surprisestat_surprise_sfStatSurpriseStatSurpriseSfsurprisesurprise_animatesurprise_rollingsurprise_temporalupdate_surprise

Dependencies:classclassIntclicpp11DBIe1071farverggplot2gluegtableisobandKernSmoothlabelinglifecycleMASSproxyR6RColorBrewerRcpprlangs2S7scalessfunitsvctrsviridisLitewithrwk

Bayesian Surprise with Canadian Census Data (cancensus)

Rendered fromcancensus-workflow.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-13
Started: 2025-12-30

Bayesian Surprise with US Census Data (tidycensus)

Rendered fromtidycensus-workflow.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-13
Started: 2025-12-30

Complete Function Reference with Examples

Rendered fromcomplete-reference.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-12
Started: 2025-12-29

Introduction to bayesiansurpriser

Rendered fromintroduction.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-12
Started: 2025-12-29

Spatial Data Workflows with sf

Rendered fromsf-workflow.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-12
Started: 2025-12-29

Temporal and Streaming Analysis

Rendered fromtemporal-analysis.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-12
Started: 2025-12-29

Understanding Model Types

Rendered frommodel-types.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-12
Started: 2025-12-29

Visualization with ggplot2

Rendered fromggplot2-visualization.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2026-04-12
Started: 2025-12-29

Readme and manuals

Help Manual

Help pageTopics
Add Model to Spaceadd_model
Compute Surprise with Automatic Model Selectionauto_surprise
Bayesian Update of Model Spacebayesian_update
Create a Base Rate Modelbs_model_baserate
Create Base Rate Model from Columnbs_model_baserate_col
Create a Bootstrap Sample Modelbs_model_bootstrap
Create a de Moivre Funnel Modelbs_model_funnel
Create Funnel Model from Columnbs_model_funnel_col
Create a Gaussian Modelbs_model_gaussian
Create Multi-Modal Gaussian Mixture Modelbs_model_gaussian_mixture
Create a Sampled Subset Model (KDE)bs_model_sampled
Create a Uniform Modelbs_model_uniform
Canadian Mischief Crime Data by Provincecanada_mischief
Compute Funnel Plot Datacompute_funnel_data
Compute Per-Region Surprisecompute_surprise
Global Bayesian Update Across All Regionscumulative_bayesian_update
Default Model Spacedefault_model_space
Example County Data with Simulated Eventsexample_counties
Compute P-Value from Funnel Z-Scorefunnel_pvalue
Funnel Z-Score (de Moivre)funnel_zscore
Surprise Map Geomgeom_surprise
Surprise Density Plotgeom_surprise_density
Surprise Histogramgeom_surprise_histogram
Get the model space from a surprise resultget_model_space
Extract surprise values from result objectsget_surprise
Get Surprise at Specific Timeget_surprise_at_time
Kullback-Leibler Divergencekl_divergence
Log-Sum-Exp (Numerically Stable)log_sum_exp
Get Model Namesmodel_names
Create a Model Spacemodel_space
Get Number of Modelsn_models
Min-Max Normalizationnormalize_minmax
Normalize to Probability Distributionnormalize_prob
Normalize to Rate (Per Capita)normalize_rate
Robust Normalization (using quantiles)normalize_robust
Z-Score Normalizationnormalize_zscore
Plot Model Spaceplot.bs_model_space
Plot Surprise Resultplot.bs_surprise
Plot Surprise Map (sf)plot.bs_surprise_sf
Plot Temporal Surpriseplot.bs_surprise_temporal
Remove Model from Spaceremove_model
Surprise Color Scale (Sequential)scale_color_surprise scale_colour_surprise scale_fill_surprise
Binned Surprise Scalescale_color_surprise_binned scale_colour_surprise_binned scale_fill_surprise_binned
Signed Surprise Color Scale (Diverging)scale_color_surprise_diverging scale_colour_surprise_diverging scale_fill_surprise_diverging
Binned Diverging Surprise Scalescale_fill_surprise_diverging_binned
Manual Surprise Breaks Scalescale_fill_surprise_manual
Signed Surprise Scale with Meaningful Thresholdsscale_color_surprise_thresholds scale_colour_surprise_thresholds scale_fill_surprise_thresholds
Set Prior Probabilitiesset_prior
Spatial Density Estimation for sf Objectsst_density
Evaluate Density at sf Feature Locationsst_density_at
Compute Surprise for sf Objectst_surprise
Compute Surprise as ggplot2 Statstat_surprise
Stat for Surprise with sf Geometriesstat_surprise_sf
Create Animation-Ready Data from Temporal Resultssurprise_animate
Rolling Window Surprisesurprise_rolling
Compute Temporal Surprisesurprise_temporal
Compute Bayesian Surprisesurprise surprise.data.frame surprise.sf surprise.tbl_df
Update Surprise with New Data (Streaming)update_surprise