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
giddy.markov.kullback(F) Kullback information based test of Markov Homogeneity.
giddy.markov.prais(pmat) Prais conditional mobility measure.
giddy.markov.homogeneity(transition_matrices) Test for homogeneity of Markov transition probabilities across regimes.
giddy.ergodic.steady_state(P) Calculates the steady state probability vector for a regular Markov transition matrix P.
giddy.ergodic.fmpt(P) Calculates the matrix of first mean passage times for an ergodic transition probability matrix.
giddy.ergodic.var_fmpt(P) 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.
giddy.rank.Tau_Local(x, y) 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.