Simple methods to generate attractive random colors. The random
colors are from a wrapper of 'randomColor.js'
<https://github.com/davidmerfield/randomColor>. In addition, it also generates
optimally distinct colors based on k-means (inspired by 'IWantHue'
<https://github.com/medialab/iwanthue>).

colorspace, stringr, V8, stats, methods, scales, Rtsne,
grDevices, cluster

The 'randomizationInference' package allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and intervals for any estimand of interest, according to the specified null and alternative hypotheses.  The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.

The 'randomizationInference' package allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for any estimand of interest, according to the specified null and alternative hypotheses.  The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.

The 'randomizationInference' package allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.

Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.