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

Partition 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 semi-parametric distributions.  Read http://arxiv.org/abs/1508.01939 for more details.

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.

Create a custom sized Core Collection based on a distance matrix and applying
  the A-NE (accession nearest entry), E-NE (entry nearest entry) or E-E (entry entry) method
  as introduced in Jansen and van Hintum (2007) <doi:10.1007/s00122-006-0433-9> and
  further elaborated on in Odong, T.L. (2012) <https://edepot.wur.nl/212422>.
  Optionally a list of preselected accessions to be included into the core can be set.
  For each accession in the computed core, if available nearby accessions are retrievable
  that can be used as an alternative.

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>.