RCT: Assign Treatments, Power Calculations, Balances, Impact Evaluation of Experiments (original) (raw)
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) <doi:10.48550/arXiv.1607.00698>.
Version: | 1.2 |
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Imports: | dplyr, purrr, glue, rlang, tidyr, stringr, MASS, pracma, estimatr, broom (≥ 1.0.0), forcats, magrittr, ggplot2, utils, tidyselect (≥ 1.0.0) |
Suggests: | knitr, rmarkdown, testthat |
Published: | 2024-02-21 |
DOI: | 10.32614/CRAN.package.RCT |
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 |
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