A collection of functions related to novel methods for estimating R(t), 
  created by the lab of Professor Laura White. Currently implemented methods include 
  two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, 
  adapted in 'STAN' from Li (2021) <doi:10.1371/journal.pcbi.1009210>, 
  and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into 'STAN' from 
  Zhou (2021) <doi:10.1371/journal.pcbi.1010434>.

Implements the whitening methods (ZCA, PCA, Cholesky,
 ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018)
 "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>,
 as well as the whitening approach to canonical correlation analysis allowing
 negative canonical correlations described in Jendoubi and Strimmer (2019)
 "A whitening approach to probabilistic canonical correlation analysis for omics
 data integration", <doi:10.1186/s12859-018-2572-9>. The package also offers
 functions to simulate random orthogonal matrices, compute (correlation) loadings
 and explained variation. It also contains four example data sets (extended UCI 
 wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).

Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.

Shinohara (2014) <doi:10.1016/j.nicl.2014.08.008>
    introduced 'WhiteStripe', an intensity-based normalization of T1 
    and T2 images, where normal 
    appearing white matter performs well, but requires segmentation.
    This method performs white matter mean and standard deviation
    estimates on data that has been rigidly-registered to the 'MNI'
    template and uses histogram-based methods.

Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022 <doi:10.32614/RJ-2021-048>). In addition to parallel methods, data wrangling and additional statistical attributes are provided.