The concept of reliable and clinically significant change
    (Jacobson & Truax, 1991) helps you answer the following questions for a
    sample with two measurements at different points in time (pre & post):
    Which proportion of my sample has a (considering the reliability of the
    instrument) probably not-just-by-chance difference in pre- vs. post-scores?
    Which proportion of my sample does not only change in a statistically
    significant way (see question one), but also in a clinically significant way
    (e.g. change from a test score regarded "dysfunctional" to a score regarded
    "functional")?
    This package allows you to very easily create a scatterplot of your sample
    in which the x-axis maps to the pre-scores, the y-axis maps to the
    post-scores and several graphical elements (lines, colors) allow you to gain
    a quick overview about reliable changes in these scores.
    An example of this kind of plot is Figure 2 of Jacobson & Truax (1991).
    Referenced article:
    Jacobson, N. S., & Truax, P. (1991) <doi:10.1037/0022-006X.59.1.12>.

A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.

A programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.

Functions to assist manipulation of matrix 
             row and column labels for all types of matrix mathematics
             where row and column labels are to be respected.

'Yandex Clickhouse' (<https://clickhouse.com/>) is a high-performance relational column-store database to enable
    big data exploration and 'analytics' scaling to petabytes of data. Methods are
    provided that enable working with 'Yandex Clickhouse' databases via
    'DBI' methods and using 'dplyr'/'dbplyr' idioms.