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Comparison · Infra & APIs
ddml vs tealeaves
A side-by-side editorial comparison of ddml and tealeaves — release velocity, themes, recent moves, and the top alternatives to consider.
ddml vs tealeaves: at a glance
What is ddml?
Double machine learning in R keeps adding estimands and the inference to go with them.
ddml implements double and debiased machine learning estimators, with a stacking layer so the nuisance functions can be fit by an ensemble rather than a single learner. The estimand list has grown from partially linear models to average treatment effects, treatment effects on the treated, and local average treatment effects, and 0.3.0 added one-way clustered inference. The most recent release is maintenance: xgboost syntax, glmnet binomial predictions, weights in the flexible partially linear IV estimator.
What is tealeaves?
A leaf-temperature model that finished its job in 2020 and has stayed finished
tealeaves solves for leaf temperature from an energy balance, using explicit units to keep parameters consistent and modelling lower and upper leaf surfaces separately so sensible and latent heat loss are computed for each. The package reached its current form in 2020 across versions 1.0.2 to 1.0.5, which added direct or functional sky temperature, dplyr 1.0.0 compatibility, and fixes to a parameter-crossing bug that the new sky temperature function had introduced. The only release since, v1.0.6 in July 2022, corrects a name in the citation file, stops parallel evaluation in a vignette and fixes README links.
ddml vs tealeaves: editorial side-by-side
ddml
0.0INFRA · APIS
Double machine learning in R keeps adding estimands and the inference to go with them.
◆ Current state
ddml implements double and debiased machine learning estimators, with a stacking layer so the nuisance functions can be fit by an ensemble rather than a single learner. The estimand list has grown from partially linear models to average treatment effects, treatment effects on the treated, and local average treatment effects, and 0.3.0 added one-way clustered inference. The most recent release is maintenance: xgboost syntax, glmnet binomial predictions, weights in the flexible partially linear IV estimator.
◆ Where it's heading
Two lines of work run in parallel. One extends what can be estimated, the other makes the estimates trustworthy under real data conditions, and the second is where the recent effort has gone: clustered standard errors, propensity score trimming, higher default fold counts, corrected ATE and LATE scores. Raising sample_folds and cv_folds to ten is a small change with a clear intent, trading compute for stability.
◆ Prediction
Clustered inference arrived one-way; two-way and multi-way clustering are the obvious continuation. The stacking layer is also accumulating edge-case handling, so expect more work on degenerate ensemble weights.
tealeaves
0.0INFRA · APIS
A leaf-temperature model that finished its job in 2020 and has stayed finished
◆ Current state
tealeaves solves for leaf temperature from an energy balance, using explicit units to keep parameters consistent and modelling lower and upper leaf surfaces separately so sensible and latent heat loss are computed for each. The package reached its current form in 2020 across versions 1.0.2 to 1.0.5, which added direct or functional sky temperature, dplyr 1.0.0 compatibility, and fixes to a parameter-crossing bug that the new sky temperature function had introduced. The only release since, v1.0.6 in July 2022, corrects a name in the citation file, stops parallel evaluation in a vignette and fixes README links.
◆ Where it's heading
This is finished scientific software. The arc runs from a 1.0.0 that already described the full model, through a usability decision in 1.0.1 to accept unitless values and assign units rather than demand them, to a 2020 cluster of compatibility and correctness work around publication. Nothing since has touched the model, and the 2022 release is pure paperwork. Its most instructive entry remains 1.0.5, where a new feature silently produced incorrect parameter crossing and the fix arrived with tests to pin the behaviour.
◆ Prediction
Expect nothing unless a dependency or CRAN check forces a release; on this record any such release will be documentation and packaging rather than a change to the energy balance.
Alternatives to ddml and tealeaves
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 ddml or tealeaves.
Warp
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Okta
Okta's developer blog is a Cross App Access campaign, now diluted by advocacy-team storytelling.
Infisical
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SigNoz
Search without knowing the field — SigNoz keeps lowering the cost of not knowing your schema
nmfspalette
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See all ddml alternatives → · See all tealeaves alternatives →
Recent activity from ddml and tealeaves
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 8mo agoddmlFixes for weighted FPLIV, binomial glmnet and empty stacking weights
- 1y agoddmlOne-way clustered inference and higher default fold counts
- 2y agoddmlPropensity score trimming added across the treatment effect estimators
- 2y agoddmlFixes permuted residuals returned by crossval
- 2y agoddmlATT and LATE estimators join the supported estimands
- 4y agotealeavesCitation file, vignette and README fixes
- 6y agotealeavesParameter-crossing bug fixed with tests; coverage added
- 6y agotealeavesFix for custom sky temperature function being overwritten
- 6y agotealeavesSky temperature as value or function; dplyr 1.0.0 compatibility
- 7y agotealeavesUnitless parameter values now accepted and assigned units
- 7y agotealeavesFirst release: leaf energy balance with per-surface conductances
Frequently asked questions
What is the difference between ddml and tealeaves?
They serve adjacent needs but don't currently overlap on shipped themes. ddml and tealeaves 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 ddml better than tealeaves?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ddml and tealeaves 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 Infra & APIs products to evaluate alongside.
What are the best alternatives to ddml?
Top ddml alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ddml alternatives" section above for the current picks, or visit /alternatives/ddml for the full list with editorial commentary on each.
What are the best alternatives to tealeaves?
Top tealeaves alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tealeaves alternatives" section above for the current picks, or visit /alternatives/tealeaves for the full list with editorial commentary on each.