Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.

Partitions data points (variables) into communities/clusters, 
    similar to clustering algorithms such as k-means and hierarchical 
    clustering. This package implements a clustering algorithm based on a new 
    metric CORD, defined for high-dimensional parametric or semiparametric 
    distributions. For more details see Bunea et al. (2020), Annals of Statistics 
    <doi:10.1214/18-AOS1794>.

Functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) <doi:10.1080/10618600.2017.1349664>. We provide an R native version with methods for printing, summarizing, and plotting the result.

Given a collection of intervals with integer start and end 
    positions, find recurrently targeted regions and estimate the significance 
    of finding. Randomization is implemented by parallel methods, either 
    using local host machines, or submitting grid engine jobs.

Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.

Create correlation heatmaps from a numeric matrix. Ensembl Gene ID row names can be converted to Gene Symbols
     using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number
     of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by
     asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default.
     The plot features rely on the heatmap.n2() function in the 'heatmapFlex' package.