knitr, rmarkdown, testthat (>= 2.1.0), covr, MASS, spelling

person("Joe", "Chelladurai", , "joe.chelladurai@outlook.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-8477-3753"))
Provides convenience functions for researching experiences
including user, customer, patient, employee, and other human experiences.
It provides a suite of tools to simplify data exploration such as
benchmarking, comparing groups, and checking for differences. The outputs
translate statistical approaches in applied experience research to
human readable output.
cli, dplyr, huxtable, magrittr, scales, stringr, tibble
Joe Chelladurai [aut, cre] (<https://orcid.org/0000-0001-8477-3753>)

experiment: R package for designing and analyzing randomized experiments
The package provides various statistical methods for
designing and analyzing randomized experiments. One main functionality
of the package is the implementation of randomized-block and
matched-pair designs based on possibly multivariate pre-treatment
covariates. The package also provides the tools to analyze various
randomized experiments including cluster randomized experiments,
randomized experiments with noncompliance, and randomized experiments
with missing data.

experiment: R package for designing and analyzing randomized
experiments
The package provides various statistical methods for
designing and analyzing randomized experiments. One main
functionality of the package is the implementation of
randomized-block and matched-pair designs based on possibly
multivariate pre-treatment covariates. The package also
provides the tools to analyze various randomized experiments
including cluster randomized experiments, randomized
experiments with noncompliance, and randomized experiments with
missing data.

R Package for Designing and Analyzing Randomized Experiments
c(
person("Kosuke", "Imai", , "kimai@princeton.edu", c("aut", "cre")),
person("Zhichao", "Jiang", , "zhichaoj@princeton.edu", c("ctb"))
)
Provides various statistical methods for
designing and analyzing randomized experiments. One main functionality
of the package is the implementation of randomized-block and
matched-pair designs based on possibly multivariate pre-treatment
covariates. The package also provides the tools to analyze various
randomized experiments including cluster randomized experiments,
randomized experiments with noncompliance, and randomized experiments
with missing data.
Kosuke Imai [aut, cre],
Zhichao Jiang [ctb]