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
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>.

dplyr, purrr, glue, rlang, tidyr, stringr, MASS, pracma, lfe,
broom, forcats, magrittr, ggplot2, utils, tidyselect (>= 1.0.0)

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>.

Designing and Analyzing Two-Stage Randomized Experiments
c(person("Karissa", "Huang", email = "krhuang@berkeley.edu", role = c("aut")),
person("Zhichao", "Jiang", email = "zhichaojiang@umass.edu", role = c("aut")),
person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut", "cre"))
)
Provides various statistical methods for designing and analyzing two-stage randomized controlled
trials using the methods developed by Imai, Jiang, and Malani (2021) <doi:10.1080/01621459.2020.1775612> and
(2022+) <doi:10.48550/arXiv.2011.07677>.  The package enables the estimation of direct and spillover effects,
conduct hypotheses tests, and conduct sample size calculation for two-stage randomized controlled trials.
Karissa Huang [aut],
Zhichao Jiang [aut],
Kosuke Imai [aut, cre]