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markov(): classic discrete-time, first-order Markov
transition estimation from tidy long-format
id/time/value panel data, with
quantile-based (fixed or per-period) class discretisation.full_rank_markov() and geo_rank_markov():
rank-based Markov chains (ranks-as-states, and
units-exchanging-rank-positions) — no binning required.lisa_markov(): Markov chain over the four
Moran-scatterplot quadrants (HH/LH/LL/HL), capturing the joint dynamics
of a unit and its neighbourhood.spatial_markov(): spatial Markov chain (Rey 2001) with
transition matrices conditioned on the spatial-lag class of the
neighbourhood. Accepts either a spatial weights matrix (lag computed
internally) or a precomputed lag column, and explicit
breaks / lag_breaks cut points. With matching
cut points it reproduces PySAL giddy’s spatial Markov
matrices to machine precision.steady_state(): ergodic / stationary distribution of a
transition matrix or an sddr_markov object.mfpt(): mean first passage times (Kemeny-Snell
fundamental matrix), with mean recurrence times on the diagonal.sojourn_time(): expected persistence time in each
class.tau(): Kendall’s tau rank correlation (positional /
exchange mobility), with concordant/discordant counts and an asymptotic
p-value.theta(): Theta rank-mobility statistic (Rey 2004)
decomposing rank change by regime.tau_local(): per-observation (local) Kendall’s tau
decomposition.mobility(): Markov mobility indices (Prais,
determinant, second-eigenvalue L2, and the Shorrocks B1/B2
indices).print() methods for sddr_markov and
sddr_spatial_markov.spatial_markov() and lisa_markov() now
accept spatial weights as an listw or nb
object (e.g. built from an sf layer), in addition to a
plain weights matrix. ‘spdep’ is an optional (Suggests) dependency.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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