Nucleotide conversion sequencing experiments have been
  developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such 
  experiments require specialized tools for primary processing such as GRAND-SLAM,
  (see 'Jürges et al' <doi:10.1093/bioinformatics/bty256>) and specialized tools for 
  downstream analyses. 'grandR' provides a comprehensive toolbox for quality control,
  kinetic modeling, differential gene expression analysis and visualization of such data.
  Fast Wilcoxon tests are supported via the 'presto' package (available at <https://github.com/immunogenomics/presto>).

Contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne' and Montanari, 2018 <arXiv:1803.00374>.

Performs Granger causality tests on pairs of time series to determine
    causal relationships. Uses Vector Autoregressive (VAR) models to test whether
    one time series helps predict another beyond what the series' own past values
    provide. Returns structured results including p-values, test statistics, and
    causality conclusions for both directions.

This small collection of functions provides what we call elemental graphics for display of analysis of variance
    results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) 
    <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. 
    The term elemental derives from the fact that each function is aimed at construction of
    graphical displays that afford direct visualizations of data with respect to the fundamental 
    questions that drive the particular analysis of variance methods. These functions can be 
    particularly helpful for students and non-statistician analysts. But these methods should be
    quite generally helpful for work-a-day applications of all kinds, as they can help to identify
    outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.