Provides a set of convenient functions for calculating sun-related information, including the sun's position (elevation and azimuth), and the times of sunrise, sunset, solar noon, and twilight for any given geographical location on Earth. These calculations are based on equations provided by the National Oceanic & Atmospheric Administration (NOAA) <https://gml.noaa.gov/grad/solcalc/calcdetails.html> as described in "Astronomical Algorithms" by Jean Meeus (1991, ISBN: 978-0-943396-35-4). A resource for researchers and professionals working in fields such as climatology, biology, and renewable energy.

Implements the self-updating process clustering algorithms proposed
    in Shiu and Chen (2016) <doi:10.1080/00949655.2015.1049605>.

Methodology for supervised grouping aka "clustering" of
   potentially many predictor variables, such as genes etc, implementing
   algorithms 'PELORA' and 'WILMA'.

Computes standard error and confidence interval of various descriptive statistics under 
    various designs and sampling schemes. The main function, superbPlot(), return a plot. superbData() 
    returns a dataframe with the statistic and its precision interval so that other plotting package
    can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> 
    or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005)
    <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012)
    <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>,
    Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding 
    <doi:10.20982/tqmp.15.1.p001> for specific references.

Biclusters are submatrices in the data matrix which
        satisfy certain conditions of homogeneity. Package contains
        functions for generating robust biclusters with respect to the
        initialization parameters for a given bicluster solution
        contained in a bicluster set in data, the procedure is also
        known as ensemble biclustering. The set of biclusters is
        evaluated based on the similarity of its elements (the
        overlap), and afterwards the hierarchical tree is constructed
        to obtain cut-off points for the classes of robust biclusters.
        The result is a number of robust (or super) biclusters with
        none or low overlap.