# API reference¶

## Markov Methods¶

 giddy.markov.Markov(class_ids[, classes]) Classic Markov transition matrices. giddy.markov.Spatial_Markov(y, w[, k, m, …]) Markov transitions conditioned on the value of the spatial lag. giddy.markov.LISA_Markov(y, w[, …]) Markov for Local Indicators of Spatial Association Full Rank Markov in which ranks are considered as Markov states rather than quantiles or other discretized classes. Geographic Rank Markov. Kullback information based test of Markov Homogeneity. Prais conditional mobility measure. giddy.markov.homogeneity(transition_matrices) Test for homogeneity of Markov transition probabilities across regimes. Calculate sojourn time based on a given transition probability matrix. Calculates the steady state probability vector for a regular Markov transition matrix P. Calculates the matrix of first mean passage times for an ergodic transition probability matrix. Variances of first mean passage times for an ergodic transition probability matrix.

## Directional LISA¶

 giddy.directional.Rose(Y, w[, k]) Rose diagram based inference for directional LISAs.

## Economic Mobility Indices¶

 giddy.mobility.markov_mobility(p[, measure, ini]) Markov-based mobility index.

## Exchange Mobility Methods¶

 giddy.rank.Theta(y, regime[, permutations]) Regime mobility measure. giddy.rank.Tau(x, y) Kendall’s Tau is based on a comparison of the number of pairs of n observations that have concordant ranks between two variables. giddy.rank.SpatialTau(x, y, w[, permutations]) Spatial version of Kendall’s rank correlation statistic. Local version of the classic Tau. giddy.rank.Tau_Local_Neighbor(x, y, w[, …]) Neighbor set LIMA. giddy.rank.Tau_Local_Neighborhood(x, y, w[, …]) Neighborhood set LIMA. giddy.rank.Tau_Regional(x, y, regime[, …]) Inter and intraregional decomposition of the classic Tau.