sate3.1.0 package

Scientific Analysis of Trial Errors (SATE)

as.jury.point

Calculates probability a jury will find defendant guilty based on juro...

as.jury.stats

Calculates probability a jury will find defendant guilty based on juro...

basic.plot.grid

Creates the shell of a plot showing relationship between jury pool pre...

compact_harm_plot

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

compare.juror.stats

Estimates juror-level differences based on sample statistics (from sur...

compare.jury.stats

Estimates jury-level differences based on juror-level statistics with ...

deliberate.civil

Deliberation function for civil trials (proposed)

deliberate

Deliberation function

encode.cloud.respondent.variables

Encodes Cloud Research respondent information in form suitable for cal...

get_pG_by_k

Calculates vector of probabilities that jury with jury_n will return a...

graph.effect.defendant

Plots jury-level differences based on juror-level statistics with effe...

graph.estimate

Plots probability of a guilty verdict with confidence interval based o...

prob_ord_from_pool

verdict probabilities based on jury pool sentiment for ordered verdict...

prob.ordered.verdicts

Absorption probabilities for ordered-category jury models

select.with.strikes

Generates the distribution of initial votes for guilty verdict on juri...

sim.as.jury.stats

Estimates jury-level probability of guilty verdict based on juror-leve...

sim.compare.jury.stats

Estimates jury-level differences based on juror-level statistics using...

target.population.demographics

Looks up and returns key demographic statistics for target state to be...

transition.matrix.ordered

Build column-stochastic transition matrix for ordered verdict options

transition.matrix

Creates and Returns a Transition Probability Matrix for Deliberating C...

weights_for_population

Calculates survey weights given respondent information and target popu...

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) <doi:10.5070/CJ88164341> 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) <https://vasocialpolicy.org/wp-content/uploads/2025/05/Edwards-If-The-Jury-Only-Knew.pdf>. The relationship between individual jurors' verdict preferences and the probability that a jury returns a guilty verdict has been studied by Davis (1973) <doi:10.1037/h0033951>; MacCoun & Kerr (1988) <doi:10.1037/0022-3514.54.1.21>, and Devine et el. (2001) <doi:10.1037/1076-8971.7.3.622>, among others.

  • Maintainer: Barry Edwards
  • License: CC0
  • Last published: 2025-11-05