Entropy-Based Segregation Indices
Compresses a data matrix based on mutual information (segregation)
Calculates Index of Dissimilarity
Calculates expected values when true segregation is zero
Calculates the entropy of a distribution
Calculates pairwise exposure indices
Create crosswalk after compression
Adjustment of marginal distributions using iterative proportional fitt...
Calculates isolation indices
Turns a contingency table into long format
Creates a compressed dataset
Decomposes the difference between two M indices
Calculates expected values when true segregation is zero
Calculates local segregation scores based on M
Calculates the Mutual Information Index M and Theil's Entropy Index H
Calculates a nested decomposition of segregation for M and H
Calculates detailed within-category segregation scores for M and H
Scree plot for segregation compression
A visual representation of two-group segregation
A visual representation of segregation
segregation: Entropy-based segregation indices
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.