Authors@R: c(person("Fabian", "Otto-Sobotka", role = "cre", email = "fabian.otto-sobotka@uni-oldenburg.de"),
person("Elmar", "Spiegel", role = "aut", email = "espiege@uni-goettingen.de"),
Depends: R (>= 3.5.0), stats, parallel, mboost (>= 2.1.0), BayesX (>=
Imports: Rcpp (>= 0.11.2), splines, quadprog, colorspace (>= 0.97),
Packaged: 2019-08-23 09:19:52 UTC; fabiansobotka

Description: Distributes samples in batches while making batches homogeneous
according to their description. Allows for an arbitrary number of variables,
both numeric and categorical. For quality control it provides functions to
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), covr, BiocStyle, MASS,

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

person("Joe", "Chelladurai", , "joe.chelladurai@outlook.com", role = c("aut", "cre"),
Description: 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
Imports: cli, dplyr, huxtable, magrittr, scales, stringr, tibble
Author: Joe Chelladurai [aut, cre] (<https://orcid.org/0000-0001-8477-3753>)

Title: experiment: R package for designing and analyzing randomized experiments
Description: 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
Packaged: Thu Jun 21 10:55:03 2007; kimai

Title: experiment: R package for designing and analyzing randomized
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
Packaged: Tue Jul 24 09:31:47 2007; kimai