# Entropy-Based Segregation Indices

## Documentation for package ‘segregation’ version 1.1.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

compress | Compresses a data matrix based on mutual information (segregation)
dissimilarity | Calculates Index of Dissimilarity
dissimilarity_expected | Calculates expected values when true segregation is zero
entropy | Calculates the entropy of a distribution
exposure | Calculates pairwise exposure indices
get_crosswalk | Create crosswalk after compression
ipf | Adjustment of marginal distributions using iterative proportional fitting
isolation | Calculates isolation indices
matrix_to_long | Turns a contingency table into long format
merge_units | Creates a compressed dataset
mutual_difference | Decomposes the difference between two M indices
mutual_expected | Calculates expected values when true segregation is zero
mutual_local | Calculates local segregation scores based on M
mutual_total | Calculates the Mutual Information Index M and Theil's Entropy Index H
mutual_total_nested | Calculates a nested decomposition of segregation for M and H
mutual_within | Calculates detailed within-category segregation scores for M and H
schools00 | Ethnic/racial composition of schools for 2000/2001
schools05 | Ethnic/racial composition of schools for 2005/2006
school_ses | Student-level data including SES status
scree_plot | Scree plot for segregation compression
segcurve | A visual representation of two-group segregation
segplot | A visual representation of segregation