A 'shiny' application, which allows you to perform single- and
    multi-omics analyses using your own omics datasets. After the upload 
    of the omics datasets and a metadata file, single-omics is performed
    for feature selection and dataset reduction. These datasets are used 
    for pairwise- and multi-omics analyses, where automatic tuning is 
    done to identify correlations between the datasets - the end goal 
    of the recommended 'Holomics' workflow.
    Methods used in the package were implemented in the package 'mixomics'
    by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752>
    and are described there in further detail.

Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.

A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.

Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (2016) <arXiv:1611.06739v2>.

Most common exact, asymptotic and resample based tests are provided for testing the 
            homogeneity of variances of k normal distributions under normality. 
            These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. 
            Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters.  
            Bartlett, M. S. (1937) <doi:10.1098/rspa.1937.0109> 
            Bhandary, M., & Dai, H. (2008) <doi:10.1080/03610910802431011>
            Brown, M. B., & Forsythe, A. B. (1974).<doi:10.1080/01621459.1974.10482955>
            Chang, C. H., Pal, N., & Lin, J. J. (2017) <doi:10.1080/03610918.2016.1202277>
            Gokpinar E. & Gokpinar F. (2017) <doi:10.1080/03610918.2014.955110>
            Liu, X., & Xu, X. (2010) <doi:10.1016/j.spl.2010.05.017>
            Levene, H. (1960) <https://cir.nii.ac.jp/crid/1573950400526848896>
            Gökpınar, E. (2020) <doi:10.1080/03610918.2020.1800037>.