An approach to analyzing Likert response items, with an emphasis on visualizations. 
    The stacked bar plot is the preferred method for presenting Likert results. Tabular results
    are also implemented along with density plots to assist researchers in determining whether 
    Likert responses can be used quantitatively instead of qualitatively. See the likert(), 
    summary.likert(), and plot.likert() functions to get started.

Synthesise rating-scale data with predefined first & second
    moments (mean & standard deviation) and, optionally, correlate
    multiple vectors with predefined correlation matrix.  Also generate
    synthetic rating-scale data with predefined Cronbach's Alpha, or
    generate rating-scale items from a predefined scale.

A comprehensive analysis tool for metabolomics data.  It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.

Estimation of life expectancy and
    Life Years Lost (LYL, or lillies for short) for a given population, for 
    example those with a given disease or condition. In addition, the package 
    can be used to compare estimates from different populations, or to estimate 
    confidence intervals. Technical details of the method are available in
    Plana-Ripoll et al. (2020) <doi:10.1371/journal.pone.0228073> and Andersen
    (2017) <doi:10.1002/sim.7357>.