rjdqa
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
Comparison · Analytics
redist vs spmodel
A side-by-side editorial comparison of redist and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
redist vs spmodel: at a glance
What is redist?
redist keeps rewriting the sampler underneath a district-drawing API it has held stable since 4.0.
redist simulates redistricting plans via sequential Monte Carlo, merge-split MCMC and short-burst optimization, and it is the analysis tool behind a good deal of published districting work. The user-facing shape was set by 4.0.1's constraint interface and the split of metrics into the redistmetrics package; since then the changes are in the algorithms. The most consequential recent one replaces the SMC label-counting adjustment with a backward kernel that removes approximation error outright.
What is spmodel?
Spatial regression in R, adding block kriging and then tuning the numerics underneath it
spmodel fits spatial linear and generalised linear models, for both point-referenced and areal data, with prediction and diagnostics attached. Block prediction arrived in 0.11.0 and the releases since have refined it. The most recent release changes optimiser behaviour: the default Nelder-Mead relative stopping tolerance tightens from 1e-4 to 1e-6 to reduce convergence on local rather than global maxima.
redist vs spmodel: editorial side-by-side
redist
0.0ANALYTICS
redist keeps rewriting the sampler underneath a district-drawing API it has held stable since 4.0.
◆ Current state
redist simulates redistricting plans via sequential Monte Carlo, merge-split MCMC and short-burst optimization, and it is the analysis tool behind a good deal of published districting work. The user-facing shape was set by 4.0.1's constraint interface and the split of metrics into the redistmetrics package; since then the changes are in the algorithms. The most consequential recent one replaces the SMC label-counting adjustment with a backward kernel that removes approximation error outright.
◆ Where it's heading
The direction is toward exactness and throughput at once — the new kernel is described as both eliminating approximation error and costing far less computation, and successive releases keep adding parallelism, most recently to the flip algorithm. Feature growth has moved into the optimization side, where short-burst gained multiple independent scorers and a Pareto frontier. The release notes are not a reliable ledger: 4.3.1 ships the identical text as 4.3.0.
◆ Prediction
Expect the remaining single-threaded algorithms to gain the chains-style parallelism that flip just received, following the pattern SMC established several releases ago.
spmodel
0.0ANALYTICS
Spatial regression in R, adding block kriging and then tuning the numerics underneath it
◆ Current state
spmodel fits spatial linear and generalised linear models, for both point-referenced and areal data, with prediction and diagnostics attached. Block prediction arrived in 0.11.0 and the releases since have refined it. The most recent release changes optimiser behaviour: the default Nelder-Mead relative stopping tolerance tightens from 1e-4 to 1e-6 to reduce convergence on local rather than global maxima.
◆ Where it's heading
Two threads run in parallel. The first is expanding what can be predicted — point predictions, then areal averages over a region via block kriging, then better accuracy and efficiency for that path as the block size default moved from 1000 to 4000 in 0.12.0. The second is numerical trustworthiness, and it is unusually prominent here: a range-constraint option for stability in 0.9.0, a corrected log determinant of the fixed effects in the restricted log likelihood in 0.11.0, a cloud semivariogram that had been doubling the semivariance fixed in 0.11.1, and now a tighter optimiser tolerance. Several of these silently changed results before they were caught.
◆ Prediction
Expect the maintainers to keep publishing explicit reproduction instructions alongside numerical default changes, as 0.13.0 does by documenting the `control = list(reltol = 1e-4)` escape hatch. The entries give no signal of expansion beyond the current model families.
Alternatives to redist and spmodel
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either redist or spmodel.
epikit
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
SimInf
SimInf 10.0 turns an epidemic simulator into a tool that fits models to real time series
mutagen
A young package porting Stata's egen row-wise helpers to the tidyverse, one function per release
broman
A statistician's personal toolbox, growing one plotting utility at a time
qtl
R/qtl is in pure custodial mode: every recent release answers a compiler, not a user
See all redist alternatives → · See all spmodel alternatives →
Recent activity from redist and spmodel
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agospmodelTighter optimiser tolerance to avoid local maxima
- 6mo agoredistParallel chains for redist_flip()
- 6mo agoredistPatch release reusing the 4.3.0 notes
- 6mo agospmodelEmpirical autocovariance function and better block kriging accuracy
- 9mo agospmodelCloud semivariogram doubling fixed; geometry warnings added
- 10mo agoredistSMC backward kernel removes label-counting approximation error
- 1y agospmodelBlock kriging for areal averages and their uncertainty ⚡
- 1y agospmodelRobust semivariogram and new covariance types for areal models
- 1y agospmodelRange constraint option and redefined covariance type names
- 2y agoredistMulti-objective short-burst search with Pareto frontier
- 3y agoredistredist_ci interface and faster loop-erased random walk
- 4y agoredistredist_constr() unifies constraints and admits user-defined ones ⚡
Frequently asked questions
What is the difference between redist and spmodel?
They serve adjacent needs but don't currently overlap on shipped themes. redist and spmodel are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is redist better than spmodel?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. redist and spmodel are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to redist?
Top redist alternatives in Analytics are ranked by recent ship velocity. Browse the "redist alternatives" section above for the current picks, or visit /alternatives/redist-r for the full list with editorial commentary on each.
What are the best alternatives to spmodel?
Top spmodel alternatives in Analytics are ranked by recent ship velocity. Browse the "spmodel alternatives" section above for the current picks, or visit /alternatives/spmodel for the full list with editorial commentary on each.