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Comparison · Infra & APIs
ddml vs logbin
A side-by-side editorial comparison of ddml and logbin — release velocity, themes, recent moves, and the top alternatives to consider.
ddml vs logbin: 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 logbin?
Relative-risk regression that converges where glm fails, under an unreadable tag order
logbin fits log-binomial models to estimate relative risk, a fit standard glm frequently fails to converge on because of the constrained parameter space. Its answer is a choice of algorithms — adaptive barrier, combinatorial EM, and expectation-maximisation on an overparameterised model — selected through a method argument and optionally accelerated with turboEM. The most recent release, in April 2025, replaces the variance-covariance calculation in summary.logbin so it matches summary.glm, and adds a testthat suite.
ddml vs logbin: 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.
logbin
0.0INFRA · APIS
Relative-risk regression that converges where glm fails, under an unreadable tag order
◆ Current state
logbin fits log-binomial models to estimate relative risk, a fit standard glm frequently fails to converge on because of the constrained parameter space. Its answer is a choice of algorithms — adaptive barrier, combinatorial EM, and expectation-maximisation on an overparameterised model — selected through a method argument and optionally accelerated with turboEM. The most recent release, in April 2025, replaces the variance-covariance calculation in summary.logbin so it matches summary.glm, and adds a testthat suite.
◆ Where it's heading
The method work concluded in 2021 and the package has since been aligned with base R conventions rather than extended: the vcov calculation now mirrors glm's, and earlier releases added the contrasts, qr, R and effects components so standard glm S3 methods such as influence() and plot() work on logbin objects. Be warned that the feed's tag order is unusable — versions 2.0, 2.0.1, 2.0.2 and 2.0.4 were all pushed within ninety seconds on 23 July 2021 in non-monotonic order, while 2.0.3 carries a 2017 timestamp and restates 2.0.2's notes. Read the bodies, not the sequence.
◆ Prediction
Expect continued alignment with glm conventions and occasional CRAN maintenance; the algorithm set has been stable for four years and nothing in these entries suggests another is planned.
Alternatives to ddml and logbin
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 logbin.
Okta
Okta's developer blog is a Cross App Access campaign, now diluted by advocacy-team storytelling.
tealeaves
A leaf-temperature model that finished its job in 2020 and has stayed finished
Infisical
A credential platform assembled two or three pull requests at a time, never a headline
SigNoz
Search without knowing the field — SigNoz keeps lowering the cost of not knowing your schema
nmfspalette
A NOAA Fisheries colour palette that ships when the branding guide changes
Recent activity from ddml and logbin
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 8mo agoddmlFixes for weighted FPLIV, binomial glmnet and empty stacking weights
- 1y agologbinvcov in summary.logbin now matches summary.glm
- 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
- 5y agologbinFactor reparameterisation fix and faster parameter expansion
- 5y agologbinVersion bump to satisfy a CRAN check
- 5y agologbinmethod and accelerate options: adaptive barrier, CEM, EM, turboEM
- 5y agologbinglm S3 method support via contrasts, qr, R and effects components
- 5y agologbinJournal of Statistical Software citation added
Frequently asked questions
What is the difference between ddml and logbin?
They serve adjacent needs but don't currently overlap on shipped themes. ddml and logbin 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 logbin?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ddml and logbin 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 logbin?
Top logbin alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "logbin alternatives" section above for the current picks, or visit /alternatives/logbin for the full list with editorial commentary on each.