Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.

Exploratory model analysis with <http://ggobi.org>.  
    Fit and graphical explore ensembles of linear models.

Create variable width bar charts i.e.
    "bar mekko" charts to include important quantitative context.
    Closely related to mosaic, spine (or spinogram), matrix,
    submarine, olympic, Mondrian or product plots and tree maps.

Performs multiple empirical likelihood tests. It offers an
    easy-to-use interface and flexibility in specifying hypotheses and
    calibration methods, extending the framework to simultaneous
    inferences.  The core computational routines are implemented using the
    'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for
    parallel computation.  Details of the testing procedures are provided
    in Kim, MacEachern, and Peruggia (2023)
    <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim,
    MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is
    available for further information. This work was supported by the U.S.
    National Science Foundation under Grants No. SES-1921523 and
    DMS-2015552.

The goal of 'meltr' is to provide a fast and friendly way to
    read non-rectangular data, such as ragged forms of csv (comma-separated
    values), tsv (tab-separated values), and fwf (fixed-width format) files.

Framework for merging and disambiguating event data based on spatiotemporal co-occurrence and secondary event characteristics. It can account for intrinsic "fuzziness" in the coding of events, varying event taxonomies and different geo-precision codes.

The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.