Add significance marks to any R Boxplot, including a given significance niveau.

Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.

Methods for the analysis of signed networks. This includes
    several measures for structural balance as introduced by Cartwright
    and Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms
    from Doreian (2008) <doi:10.1016/j.socnet.2008.03.005>, various
    centrality indices, and projections of signed two-mode networks
    introduced by Schoch (2020) <doi:10.1080/0022250X.2019.1711376>.

Provides convenience functions to replace hyphen-minuses (ASCII 45)
    with proper minus signs (Unicode character 2212). The true minus matches
    the plus symbol in width, line thickness, and height above the baseline.
    It was designed for mathematics, looks better in presentation,
    and is understood properly by screen readers.

Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.

Pathway Analysis is statistically linking observations on 
     the molecular level to biological processes or pathways on 
     the systems(i.e., organism, organ, tissue, cell) level.  
     Traditionally, pathway analysis methods regard pathways 
     as collections of single genes and treat all genes in a pathway
     as equally informative. However, this can lead to identifying
     spurious pathways as statistically significant since components
     are often shared amongst pathways. SIGORA seeks to avoid this
     pitfall by focusing on genes or gene pairs that are (as a combination)
     specific to a single pathway.
     In relying on such pathway gene-pair signatures (Pathway-GPS),
     SIGORA inherently uses the status of other genes in the
     experimental context to identify the most relevant pathways.
     The current version allows for pathway analysis of human
     and mouse datasets. In addition, it contains pre-computed
     Pathway-GPS data for pathways in the KEGG and  Reactome
     pathway repositories and mechanisms for extracting GPS
     for user-supplied repositories.