aihuman: Experimental Evaluation of Algorithm-Assisted Human
Decision-Making
Provides statistical methods for analyzing experimental
evaluation of the causal impacts of algorithmic recommendations
on human decisions developed by Imai, Jiang, Greiner, Halen, and
Shin (2023) <doi:10.1093/jrsssa/qnad010>.
The data used for this paper, and made available here, are interim,
based on only half of the observations in the study and (for those
observations) only half of the study follow-up period. We use them
only to illustrate methods, not to draw substantive conclusions.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
Rcpp, coda, stats, magrittr, purrr, abind, foreach, parallel, doParallel, ggplot2, dplyr, tidyr, metR, MASS, lme4 |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppEigen |
| Suggests: |
knitr, rmarkdown |
| Published: |
2023-03-02 |
| Author: |
Sooahn Shin  [aut,
cre],
Zhichao Jiang [aut],
Kosuke Imai [aut] |
| Maintainer: |
Sooahn Shin <sooahnshin at g.harvard.edu> |
| BugReports: |
https://github.com/sooahnshin/aihuman/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/sooahnshin/aihuman |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
aihuman results |
Documentation:
Downloads:
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