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Joint species distribution models in Gibbs-sampled C++, quiet since 2023.
Comparison · Infra & APIs
mice vs Tailscale
A side-by-side editorial comparison of mice and Tailscale — release velocity, themes, recent moves, and the top alternatives to consider.
mice vs Tailscale: at a glance
| Feature | mice | Tailscale |
|---|---|---|
| Sector | Infra & APIs | Infra & APIs |
| Velocity score | 0.0 | 6.3 |
| Sparks · 30d | 0 | 0 |
| Top themes | missing-data, multiple-imputation, statistics, r-package | networking, scale, api, kubernetes |
| Last editorial update | 57m ago | 7h ago |
| Website | Visit → | — |
What is mice?
mice can finally predict, not just estimate, from multiply imputed data.
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
What is Tailscale?
Tailscale is paying down scale in two dimensions: nodes per tailnet, tailnets per org.
Three threads run through this window. The tailnet management API is the newest: creation landed in alpha in late July, and the list endpoint now paginates at 100 results with limit and cursor parameters. The client releases are patch-grade but weighted toward scale — v1.102.1 made node additions and removals constant-time, and v1.102.3 fixes Tailnet Lock startup failures on large tailnets while cutting memory use on iOS and tvOS. The Kubernetes operator runs on its own track, adding in-cluster PeerRelays, workload identity federation and IPv6 egress.
mice vs Tailscale: editorial side-by-side
mice
0.0INFRA · APIS
mice can finally predict, not just estimate, from multiply imputed data.
◆ Current state
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
◆ Where it's heading
Two things are happening. The imputation method catalogue keeps widening — lasso variants, multivariate PMM, categorical PMM via canonical correlation — while the pooling side is being extended past its original purpose, first to synthetic data, now to predictions on held-out sets. That second thread points at predictive modelling workflows rather than the inferential ones mice was built for. Meanwhile the maintainers keep finding consequential old bugs: the augment() ordered-factor defect in 3.18.0 had been silently degrading ordinal imputations for years.
◆ Prediction
predict_mi() is framed around evaluating predictive performance on test sets, and the ignore argument added in 3.12.0 already exists to hold out rows from the imputation model. Expect the next work to join those up into a fuller train/test story for imputed data, since the pieces are now in place but not yet connected.
Tailscale
6.3INFRA · APIS
Tailscale is paying down scale in two dimensions: nodes per tailnet, tailnets per org.
◆ Current state
Three threads run through this window. The tailnet management API is the newest: creation landed in alpha in late July, and the list endpoint now paginates at 100 results with limit and cursor parameters. The client releases are patch-grade but weighted toward scale — v1.102.1 made node additions and removals constant-time, and v1.102.3 fixes Tailnet Lock startup failures on large tailnets while cutting memory use on iOS and tvOS. The Kubernetes operator runs on its own track, adding in-cluster PeerRelays, workload identity federation and IPv6 egress.
◆ Where it's heading
The qualifier that keeps recurring is “large”: tailnets big enough to break Tailnet Lock at startup, node churn that pinned CPU, mobile clients running short of memory, and organizations holding more than a hundred tailnets. Tailscale is absorbing the cost of customers who outgrew the shape the product originally assumed, in two directions at once — nodes inside a tailnet, and tailnets inside an organization. The second is the more consequential, because allocating a tailnet per customer or per environment is a different product than a company network. Security work stays continuous alongside it, with TS-2026-011 closed here and a run of SSH and Serve advisories backported the month before.
◆ Prediction
The tailnet creation API should leave alpha carrying the same limit-and-cursor contract just applied to the list endpoint, with further startup and memory work aimed at large tailnets on the client side.
Alternatives to mice and Tailscale
Other Infra & APIs 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 mice or Tailscale.
rsofun
An ecosystem model starts tracking carbon isotopes and land-use change.
usmap
Ten years in, US mapping splits its data out and finally adds Puerto Rico.
trackeR
Fitness-tracking analysis in slow maintenance, still absorbing upstream breakage.
states
State-panel tooling holding steady since its 2020 data and ergonomics release.
multimark
Five years of compiler and CRAN fixes on a capture-recapture package.
See all mice alternatives → · See all Tailscale alternatives →
Recent activity from mice and Tailscale
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1d agoTailscalev1.102.3 patches a 4via6 routing flaw and large-tailnet startups
- 2d agoTailscaleTailnet list API pagination
- 9d agoTailscaleOperator adds in-cluster PeerRelays and workload identity federation
- 13d agoTailscaleContainer image v1.102.2: library updates only
- 16d agoTailscalev1.102.2 fixes a Funnel incoming-connection regression
- 17d agoTailscalev1.102.1 adds Services CLI and constant-time node churn
- 8mo agomicemice 3.19.0 ⚡
- 1y agomicemice 3.18.0
- 1y agomicemice 3.17.0
- 3y agomicemice 3.16.0
- 3y agomicemice 3.15.0
- 4y agomicemice 3.14.0
Frequently asked questions
What is the difference between mice and Tailscale?
They serve adjacent needs but don't currently overlap on shipped themes. Tailscale is currently shipping more aggressively (velocity 6.3 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is mice better than Tailscale?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Tailscale is currently shipping more aggressively (velocity 6.3 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
What are the best alternatives to mice?
Top mice alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "mice alternatives" section above for the current picks, or visit /alternatives/mice for the full list with editorial commentary on each.
What are the best alternatives to Tailscale?
Top Tailscale alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "Tailscale alternatives" section above for the current picks, or visit /alternatives/tailscale for the full list with editorial commentary on each.