Entropy-Based Segregation Indices

Documentation for package ‘segregation’ version 1.1.0

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