All functions

compress()

Compresses a data matrix based on mutual information (segregation)

dissimilarity()

Calculate Dissimilarity Index

dissimilarity_expected()

Calculate expected values when true segregation is zero

entropy()

Calculates the entropy of a distribution

exposure()

Calculates pairwise exposure

get_crosswalk()

Create crosswalk after compression

ipf()

Adjustment of marginal distributions using iterative proportional fitting

isolation()

Calculates isolation

matrix_to_long()

Turns a contingency table into long format

merge_units()

merge_units

mutual_difference()

Decomposes the difference between two M indices

mutual_expected()

Calculate expected values when true segregation is zero

mutual_local()

Calculates local segregation indices based on M

mutual_total()

Calculate total segregation for M and H

mutual_total_nested()

Calculate a nested decomposition of segregation for M and H

mutual_within()

Calculate detailed within-category segregation scores for M and H

school_ses

Student-level data including SES status

schools00

Ethnic/racial composition of schools for 2000/2001

schools05

Ethnic/racial composition of schools for 2005/2006

scree_plot()

Scree plot for segregation compression

segcurve()

A visual representation of two-group segregation

segplot()

A visual representation of segregation

segregation

segregation: Entropy-based segregation indices