A system that computes metrics to assess the segmentation 
    accuracy of geospatial data. These metrics calculate the discrepancy 
    between segmented and reference objects, and indicate the segmentation 
    accuracy. For more details on choosing evaluation metrics, we 
    suggest seeing Costa et al. (2018) <doi:10.1016/j.rse.2017.11.024> and 
    Jozdani et al. (2020) <doi:10.1016/j.isprsjprs.2020.01.002>.

Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure.

Tools for modeling non-continuous linear responses of ecological communities to environmental data. The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) <doi:10.2307/1941559> and Legendre and Legendre (2012, ISBN: 9780444538697).

Computes segregation indices, including the Index of Dissimilarity,
    as well as the information-theoretic indices developed by
    Theil (1971) <isbn:978-0471858454>, namely
    the Mutual Information Index (M) and Theil's Information Index (H).
    The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x>
    and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>,
    is a measure of segregation that is highly decomposable. The package provides
    tools to decompose the index by units and groups (local segregation),
    and by within and between terms. The package also provides a method to decompose
    differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>.
    The package includes standard error estimation by bootstrapping, which also corrects for
    small sample bias. The package also contains functions for visualizing segregation patterns.