A collection of reweighted marginal hypothesis tests for clustered data, based
    on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) <doi:10.1111/1541-0420.00005>.
    The tests in this collection are clustered analogs to well-known hypothesis tests
    in the classical setting, and are appropriate for data with cluster- and/or group-size 
    informativeness. The syntax and output of functions are modeled after common, 
    recognizable functions native to R. Methods used in the package refer to 
    Gregg, M., Datta, S., and Lorenz, D. (2020) <doi:10.1177/0962280220928572>, 
    Nevalainen, J., Oja, H., and Datta, S. (2017) <doi:10.1002/sim.7288>
    Dutta, S. and Datta, S. (2015) <doi:10.1111/biom.12447>, 
    Lorenz, D., Datta, S., and Harkema, S. (2011) <doi:10.1002/sim.4368>, 
    Datta, S. and Satten, G. (2008) <doi:10.1111/j.1541-0420.2007.00923.x>, 
    Datta, S. and Satten, G. (2005) <doi:10.1198/016214504000001583>.

Estimating heterogeneous treatment effects with tree-based machine
    learning algorithms and visualizing estimated results in flexible and 
    presentation-ready ways. For more information, see Brand, Xu, Koch, 
    and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package 
    first started as a fork of the 'causalTree' package on 'GitHub' and we 
    greatly appreciate the authors for their extremely useful and free package.

Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. 
  (2003) <doi:10.1186/gb-2003-4-4-r28>) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005)
  <doi:10.1186/1471-2105-6-168>) implement 'GoMiner' and 'High Throughput GoMiner'
  ('HTGM') to map lists of genes to the Gene Ontology (GO) <https://geneontology.org>. Until recently,
  these were hosted on a server at The National Cancer Institute (NCI). In order to continue
  providing these services to the bio-medical community, I have developed stand-alone versions.
  The current package 'HTGM' builds upon my recent package 'GoMiner'.
  The output of 'GoMiner' is a heatmap showing the relationship of a single list
  of genes and the significant categories into which they map. 'High Throughput GoMiner'
  ('HTGM') integrates the results of the individual 'GoMiner' analyses. The output of 'HTGM'
  is a heatmap showing the relationship of the significant categories derived from each gene list.
  The heatmap has only 2 axes, so the identity of the genes are unfortunately
  "integrated out of the equation." Because the graphic for the heatmap is implemented
  in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each
  picture element to the relevant list of genes. By clicking on the desired picture
  element, the user can recover the "lost" genes.