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.

'Climacell' is a weather platform that provides hyper-local forecasts and weather 
    data. This package enables the user to query the core layers of the 
    time line interface of the 'Climacell' v4 API <https://www.climacell.co/weather-api/>. 
    This package requires a valid API key. See vignettes for instructions on use.

Leverages the functionality of 'clipboard.js', a JavaScript library
    for HMTL5-based copy to clipboard from web pages (see <https://clipboardjs.com>
    for more information), and provides a reactive copy-to-clipboard UI button 
    component, called 'rclipButton', and a a reactive copy-to-clipboard UI link 
    component, called 'rclipLink', for 'shiny' R applications.

Graphical toolbox for clustering and classification of data frames.
    It proposes a graphical interface to process clustering and classification methods on features
    data-frames, and to view initial data as well as resulted cluster or classes. According to the
    level of available labels, different approaches are proposed: unsupervised clustering,
    semi-supervised clustering and supervised classification. 
    To assess the processed clusters or classes, the toolbox can import and show some supplementary
    data formats: either profile/time series, or images. 
    These added information can help the expert to label clusters (clustering), or to constrain data
    frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by 
    Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by 
    Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.