Calculates the co-ranking matrix to assess the
    quality of a dimensionality reduction.

Allows the user to apply nice color gradients to 'shiny' elements.
    The gradients are extracted from the 'colorffy' website. See <https://www.colorffy.com/gradients/catalog>.

Provides a bundle of basic and fundamental bioinformatics tools,
    such as network querying and alignment, subnetwork extraction and search,
    network biomarker identification.

We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S.,  Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method.

Corbae-Ouliaris frequency domain filtering. According to 
             Corbae and Ouliaris (2006) <doi:10.1017/CBO9781139164863.008>,
             this is a solution for extracting cycles from time series, like
             business cycles etc. when filtering. This method is valid for both
             stationary and non-stationary time series.

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.