R: Experimental Evaluation of Algorithm-Assisted Human Decision-Making

# Experimental Evaluation of Algorithm-Assisted Human Decision-Making

## Documentation for package ‘aihuman’ version 1.0.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

aihuman-package | Experimental Evaluation of Algorithm-Assisted Human Decision-Making
AiEvalmcmc | Gibbs sampler for the main analysis
aihuman | Experimental Evaluation of Algorithm-Assisted Human Decision-Making
APCEsummary | Summary of APCE
APCEsummaryipw | Summary of APCE for frequentist analysis
BootstrapAPCEipw | Bootstrap for estimating variance of APCE
BootstrapAPCEipwRE | Bootstrap for estimating variance of APCE with random effects
BootstrapAPCEipwREparallel | Bootstrap for estimating variance of APCE with random effects
CalAPCE | Calculate APCE
CalAPCEipw | Compute APCE using frequentist analysis
CalAPCEipwRE | Compute APCE using frequentist analysis with random effects
CalAPCEparallel | Calculate APCE using parallel computing
CalDelta | Calculate the delta given the principal stratum
CalDIM | Calculate diff-in-means estimates
CalDIMsubgroup | Calculate diff-in-means estimates
CalFairness | Calculate the principal fairness
CalOptimalDecision | Calculate optimal decision & utility
CalPS | Calculate the proportion of principal strata (R)
compute_bounds_aipw | Compute Risk (AI v. Human)
compute_nuisance_functions | Fit outcome/decision and propensity score models
compute_nuisance_functions_ai | Fit outcome/decision and propensity score models conditioning on the AI recommendation
compute_stats | Compute Risk (Human+AI v. Human)
compute_stats_agreement | Agreement of Human and AI Decision Makers
compute_stats_aipw | Compute Risk (Human+AI v. Human)
compute_stats_subgroup | Compute Risk (Human+AI v. Human) for a Subgroup Defined by AI Recommendation
crossfit | Crossfitting for nuisance functions
FTAdata | Interim Dane data with failure to appear (FTA) as an outcome
gbm | Crossfitting for nuisance functions
g_legend | Pulling ggplot legend
HearingDate | Interim court event hearing date
hearingdate_synth | Synthetic court event hearing date
NCAdata | Interim Dane data with new criminal activity (NCA) as an outcome
NVCAdata | Interim Dane data with new violent criminal activity (NVCA) as an outcome
PlotAPCE | Plot APCE
PlotDIMdecisions | Plot diff-in-means estimates
PlotDIMoutcomes | Plot diff-in-means estimates
PlotFairness | Plot the principal fairness
PlotOptimalDecision | Plot optimal decision
PlotPS | Plot the proportion of principal strata (R)
PlotSpilloverCRT | Plot conditional randomization test
PlotSpilloverCRTpower | Plot power analysis of conditional randomization test
PlotStackedBar | Stacked barplot for the distribution of the decision given psa
PlotStackedBarDMF | Stacked barplot for the distribution of the decision given DMF recommendation
PlotUtilityDiff | Plot utility difference
PlotUtilityDiffCI | Plot utility difference with 95% confidence interval
plot_agreement | Visualize Agreement
plot_diff_ai_aipw | Visualize Difference in Risk (AI v. Human)
plot_diff_human | Visualize Difference in Risk (Human+AI v. Human)
plot_diff_human_aipw | Visualize Difference in Risk (Human+AI v. Human)
plot_diff_subgroup | Visualize Difference in Risk (Human+AI v. Human) for a Subgroup Defined by AI Recommendation
plot_preference | Visualize Preference
PSAdata | Interim Dane PSA data
psa_synth | Synthetic PSA data
SpilloverCRT | Conduct conditional randomization test
SpilloverCRTpower | Conduct power analysis of conditional randomization test
synth | Synthetic data
table_agreement | Table of Agreement
TestMonotonicity | Test monotonicity
TestMonotonicityRE | Test monotonicity with random effects