RCT: Assign Treatments, Power Calculations, Balances, Impact
Evaluation of Experiments
Assists in the whole process of designing and evaluating Randomized Control Trials.
Robust treatment assignment by strata/blocks, that handles misfits;
Power calculations of the minimum detectable treatment effect or minimum populations;
Balance tables of T-test of covariates;
Balance Regression: (treatment ~ all x variables) with F-test of null model;
Impact_evaluation: Impact evaluation regressions. This function
gives you the option to include control_vars, fixed effect variables,
cluster variables (for robust SE), multiple endogenous variables and
multiple heterogeneous variables (to test treatment effect heterogeneity)
summary_statistics: Function that creates a summary statistics table with statistics
rank observations in n groups: Creates a factor variable with n groups. Each group has
a min and max label attach to each category.
Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.
| Version: |
1.1.2 |
| Imports: |
dplyr, purrr, glue, rlang, tidyr, stringr, MASS, pracma, lfe, broom, forcats, magrittr, ggplot2, utils, tidyselect (≥ 1.0.0) |
| Suggests: |
knitr, rmarkdown, testthat |
| Published: |
2022-05-13 |
| Author: |
Isidoro Garcia-Urquieta [aut, cre] |
| Maintainer: |
Isidoro Garcia-Urquieta <isidoro.gu at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Citation: |
RCT citation info |
| Materials: |
README NEWS |
| CRAN checks: |
RCT results |
Documentation:
Downloads:
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