The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.

Provide simple functions to (i) compute a class of 
  multi-functionality measures for a single ecosystem for given 
  function weights, (ii) decompose gamma multi-functionality 
  for pairs of ecosystems and K ecosystems (K can be greater than 2) 
  into a within-ecosystem component (alpha multi-functionality) 
  and an among-ecosystem component (beta multi-functionality). 
  In each case, the correlation between functions can be corrected for. 
  Based on biodiversity and ecosystem function data, this software 
  also facilitates graphics for assessing biodiversity-ecosystem 
  functioning relationships across scales.

Multivariate functional principal component analysis via fast covariance estimation for
             multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.

Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.

Implements multi-factor curve analysis for grouped data in 'R', replicating and
    extending the functionality of the the 'Stata' ado 'mfcurve' (Krähmer, 2023) <https://ideas.repec.org/c/boc/bocode/s459224.html>. 
	Related to the idea of specification curve analysis (Simonsohn, Simmons, and Nelson, 2020) <doi:10.1038/s41562-020-0912-z>.
	Includes data preprocessing, statistical testing, and visualization of results with confidence intervals.