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sate (version 3.1.0)

Scientific Analysis of Trial Errors (SATE)

Description

Bundles functions used to analyze the harmfulness of trial errors in criminal trials. Functions in the Scientific Analysis of Trial Errors ('sate') package help users estimate the probability that a jury will find a defendant guilty given jurors' preferences for a guilty verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical trial conditions to conduct harmful error analysis. The conceptual framework is discussed by Barry Edwards, A Scientific Framework for Analyzing the Harmfulness of Trial Errors, UCLA Criminal Justice Law Review (2024) and Barry Edwards, If The Jury Only Knew: The Effect Of Omitted Mitigation Evidence On The Probability Of A Death Sentence, Virginia Journal of Social Policy & the Law (2025) . The relationship between individual jurors' verdict preferences and the probability that a jury returns a guilty verdict has been studied by Davis (1973) ; MacCoun & Kerr (1988) , and Devine et el. (2001) , among others.

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Version

Install

install.packages('sate')

Monthly Downloads

264

Version

3.1.0

License

CC0

Maintainer

Barry Edwards

Last Published

November 5th, 2025

Functions in sate (3.1.0)

sim.compare.jury.stats

Estimates jury-level differences based on juror-level statistics using simulations based on empirical data
observed.deliberations

Dataset of Observed Deliberations
select.with.strikes

Generates the distribution of initial votes for guilty verdict on juries
prob_ord_from_pool

verdict probabilities based on jury pool sentiment for ordered verdict options.
prob.ordered.verdicts

Absorption probabilities for ordered-category jury models
graph.effect.defendant

Plots jury-level differences based on juror-level statistics with effect-on-defendant displayed
state.demographic.info

State Demographic Information
target.population.demographics

Looks up and returns key demographic statistics for target state to be used for calculating sample weights
weights_for_population

Calculates survey weights given respondent information and target population demographics
transition.matrix.ordered

Build column-stochastic transition matrix for ordered verdict options
transition.matrix

Creates and Returns a Transition Probability Matrix for Deliberating Criminal Jury.
compare.juror.stats

Estimates juror-level differences based on sample statistics (from survey)
as.jury.point

Calculates probability a jury will find defendant guilty based on juror preferences
deliberate

Deliberation function
compact_harm_plot

Creates the shell of a plot used to display estimate of harm relative to harm threshold
get_pG_by_k

Calculates vector of probabilities that jury with jury_n will return a guilty verdict
deliberate.civil

Deliberation function for civil trials (proposed)
compare.jury.stats

Estimates jury-level differences based on juror-level statistics with inferential statistics
as.jury.stats

Calculates probability a jury will find defendant guilty based on juror preferences, with standard error and confidence interval
basic.plot.grid

Creates the shell of a plot showing relationship between jury pool preferences and jury verdict probabilities
encode.cloud.respondent.variables

Encodes Cloud Research respondent information in form suitable for calculating sampling weights
sim.as.jury.stats

Estimates jury-level probability of guilty verdict based on juror-level statistics based on empirical data
graph.estimate

Plots probability of a guilty verdict with confidence interval based on juror-level statistics