It provides functions to generate a correlation matrix
    from a genetic dataset and to use this matrix to predict the phenotype of an
    individual by using the phenotypes of the remaining individuals through
    kriging. Kriging is a geostatistical method for optimal prediction or best
    unbiased linear prediction. It consists of predicting the value of a
    variable at an unobserved location as a weighted sum of the variable at
    observed locations. Intuitively, it works as a reverse linear regression:
    instead of computing correlation (univariate regression coefficients are
    simply scaled correlation) between a dependent variable Y and independent
    variables X, it uses known correlation between X and Y to predict Y.

A tool for interactive exploration of the results from 'omics'
  experiments to facilitate novel discoveries from high-throughput biology. The
  software includes R functions for the 'bioinformatician' to deposit study
  metadata and the outputs from statistical analyses (e.g. differential
  expression, enrichment). These results are then exported to an interactive
  JavaScript dashboard that can be interrogated on the user's local machine or
  deployed online to be explored by collaborators. The dashboard includes
  'sortable' tables, interactive plots including network visualization, and
  fine-grained filtering based on statistical significance.

Provides an end-to-end workflow for integrative analysis of
	two omics layers using sparse canonical correlation analysis (sCCA),
	including sample alignment, feature selection, network edge construction,
	and visualization of gene-metabolite relationships. The underlying methods
	are based on penalized matrix decomposition and sparse CCA
	(Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008>),
	with design principles inspired by multivariate integrative frameworks
	such as mixOmics (Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>).

A method for the quantitative prediction using omics data. 
    This package provides functions to construct the quantitative prediction 
    model using omics data.