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sddr

The complete toolkit for distribution dynamics in R — analysing how a distribution of values across units (regions, firms, markets, assets) evolves over time, and where it is heading in the long run.

sddr brings distribution-dynamics analysis into one tidy, long-format framework and pushes it beyond what any existing package offers — with continuous stochastic kernels, continuous-time transitions, and modern inference that current R (and Python) tooling simply does not provide.

What makes `sddr` different — the new ideas

These are the capabilities that set sddr apart. (Status: ✅ available now · 🔜 in active development.)

🔜 Continuous stochastic kernels — density-based distribution dynamics (Quah), estimating the full conditional density with no arbitrary class binning. The modern approach, almost absent from R.
🔜 Continuous-time & irregular-interval Markov — for panels not observed on a regular time grid.
🔜 Modern inference — bootstrap and Bayesian intervals for transition probabilities, plus formal tests for Markov order, time-stationarity, and whether space matters at all.
🔜 Change-point / non-stationarity detection in transition dynamics.
🔜 Simulation engine, animated & interactive visualisation, broom tidiers, and an optional compiled backend for large panels.

Established methods, done right

A complete, tidy, sf-friendly implementation of the classical toolkit:

✅ Classic Markov chains and ergodic (steady-state) analysis
✅ Spatial Markov — transitions conditioned on neighbourhood context
🔜 LISA / full-rank / geo-rank Markov · rank & exchange mobility (Tau family) · mobility indices (Prais, Shorrocks) · directional LISA · sequence analysis · mean first-passage & sojourn times

All from long-format id / time / value data — no transition-matrix bookkeeping.

Installation

# install.packages("pak")
pak::pak("mqfarooqi1/sddr")

Quick start

library(sddr)

df <- data.frame(
  id    = rep(1:200, each = 5),
  time  = rep(2000:2004, times = 200),
  value = rnorm(1000)
)

# Classic Markov chain over distribution quintiles.
m <- markov(df, id = "id", time = "time", value = "value", k = 5)
m
steady_state(m)              # long-run distribution

Roadmap

Release	Focus
v0.1	The complete classical toolkit in one tidy API (Markov, spatial Markov, rank mobility, ergodic, sequences)
v0.2+	The new ideas: continuous stochastic kernels, continuous-time Markov, modern inference, change-point detection
later	Compiled backend, simulation engine, animated/interactive visualisation

Validation & prior work

sddr’s methods build directly on the distribution-dynamics literature — Quah (1993) for distributional convergence and Rey (2001) for spatial Markov dynamics.

As a correctness guarantee, where methods overlap with PySAL’s giddy (the established reference implementation) sddr is checked for numerical parity: the classic and spatial Markov estimators currently reproduce giddy to machine precision. sddr extends well past giddy and the R package griddy with the continuous-kernel, continuous-time, and inference methods above.

License

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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