API reference¶

Markov Methods¶

 giddy.markov.Markov(class_ids[, classes, …]) Classic Markov Chain estimation. 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.FullRank_Markov(y[, …]) Full Rank Markov in which ranks are considered as Markov states rather than quantiles or other discretized classes. giddy.markov.GeoRank_Markov(y[, …]) Geographic Rank Markov. 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.markov.sojourn_time(p[, summary]) Calculate sojourn time based on a given transition probability matrix. giddy.ergodic.steady_state(P[, …]) Generalized function for calculating the steady state distribution for a regular or reducible Markov transition matrix P. giddy.ergodic.fmpt(P[, fill_empty_classes]) Generalized function for calculating first mean passage times for an ergodic or non-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. 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.

Alignment-based Sequence Methods¶

 giddy.sequence.Sequence(y[, subs_mat, …]) Pairwise sequence analysis.

Utility Functions¶

 giddy.util.shuffle_matrix(X, ids) Random permutation of rows and columns of a matrix giddy.util.get_lower(matrix) Flattens the lower part of an n x n matrix into an n*(n-1)/2 x 1 vector. Assign 1 to diagonal elements which fall in rows full of 0s to ensure the transition probability matrix is a stochastic one.