finnts
Microsoft's automated forecasting framework, still mostly a one-maintainer effort.
Comparison · Infra & APIs
adjustedCurves vs missSBM
A side-by-side editorial comparison of adjustedCurves and missSBM — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
adjustedCurves vs missSBM: at a glance
What is adjustedCurves?
A survival curve package spending release after release correcting its own estimates
adjustedCurves computes confounder-adjusted survival and cumulative incidence curves across a range of estimators - IPTW, AIPTW, Aalen-Johansen, direct standardisation - with support for multiple imputation and bootstrapping. The recent releases are dominated by corrections to numbers the package already reported. Version 0.11.4 fixed cumulative incidence estimates under method="aalen_johansen" that were being read one time step early, which the maintainer notes could differ substantially when events are few, and added risk and event counts to the ggsurvplot conversion including correctly pooled values under multiple imputation.
What is missSBM?
missSBM returns after four dormant years with a stricter API and a new refinement step.
The package fits stochastic block models to networks with missing data, covering both missing-at-random and informative sampling designs. After a run of releases from 2019 to 2022, the feed goes quiet until this year's 1.1.0, which breaks the control interface, exposes the block split and merge operations as testable instance methods, and adds a node-swap refinement pass that runs after variational convergence.
adjustedCurves vs missSBM: editorial side-by-side
adjustedCurves
0.0INFRA · APIS
A survival curve package spending release after release correcting its own estimates
◆ Current state
adjustedCurves computes confounder-adjusted survival and cumulative incidence curves across a range of estimators - IPTW, AIPTW, Aalen-Johansen, direct standardisation - with support for multiple imputation and bootstrapping. The recent releases are dominated by corrections to numbers the package already reported. Version 0.11.4 fixed cumulative incidence estimates under method="aalen_johansen" that were being read one time step early, which the maintainer notes could differ substantially when events are few, and added risk and event counts to the ggsurvplot conversion including correctly pooled values under multiple imputation.
◆ Where it's heading
Multiple imputation is the recurring fault line. The standard error pooling formula was implemented incorrectly until 0.11.2, then fixed again in 0.11.3 for the bootstrapping-plus-imputation combination, and 0.11.4 added the pooled risk table values that had previously been omitted entirely. A separate thread quietly removed capability: tmle and ostmle methods went in 0.10.0, and tmle support was pulled again in 0.11.1 after the concrete package left CRAN. Feature work does happen - risk tables, contrast arguments, the extend_to_last control on IPTW curves - but it is outweighed by correction.
◆ Prediction
Expect continued estimator-level corrections rather than new methods, and a possible return of tmle support if its upstream dependency returns to CRAN, since the removal was described as temporary.
missSBM
2.5INFRA · APIS
missSBM returns after four dormant years with a stricter API and a new refinement step.
◆ Current state
The package fits stochastic block models to networks with missing data, covering both missing-at-random and informative sampling designs. After a run of releases from 2019 to 2022, the feed goes quiet until this year's 1.1.0, which breaks the control interface, exposes the block split and merge operations as testable instance methods, and adds a node-swap refinement pass that runs after variational convergence.
◆ Where it's heading
The new release is maintenance-driven in the best sense: it targets the parts of the codebase that were hard to test or easy to misuse. Replacing free-form control lists with a function of named, defaulted arguments turns silent typos into errors, and pulling the exploration logic out of the collection class makes the search algorithm independently testable without changing it. The polish step addresses a known weakness, reaching individually misclassified nodes that split and merge moves cannot fix.
◆ Prediction
Given the gap before this release, the near-term question is whether the cadence resumes at all; the refactoring it contains would support further algorithmic work if it does.
Alternatives to adjustedCurves and missSBM
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 adjustedCurves or missSBM.
BayLum
Bayesian luminescence dating that finally replaced its folder-structure input format.
daedalus
An epidemic-economic model teaching its interventions to react to the outbreak itself.
bkmrhat
A parallel-chain helper for bkmr that has settled into pure upkeep.
driveR
A cancer driver prioritization package that ships rarely and mostly to stay installable
metR
A meteorology ggplot2 extension where the netCDF reader became the main event
See all adjustedCurves alternatives → · See all missSBM alternatives →
Recent activity from adjustedCurves and missSBM
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 26d agomissSBMReplaces raw control lists with missSBM_param(), adds polish()
- 6mo agoadjustedCurvesOff-by-one-step error corrected in cumulative incidence estimates
- 1y agoadjustedCurvesIPTW curves can now extend to the last observed time
- 2y agoadjustedCurvesDropped arguments and a wrong multiple-imputation pooling formula
- 2y agoadjustedCurvesRisk tables, contrast consolidation and faster bootstrapping
- 3y agoadjustedCurvestmle and ostmle methods dropped
- 3y agoadjustedCurvesDependency compatibility and installation documentation
- 3y agomissSBMAdapts to Matrix 1.4-2 and fixes HTML5 documentation
- 4y agomissSBMFixes linking against nloptR 2.0.0
- 5y agomissSBMRelaxes CRAN test tolerances to avoid random failures
- 5y agomissSBMRewrites optimisation in C++ armadillo with sparse matrices
- 5y agomissSBMRenames core functions and interfaces with the sbm package
Frequently asked questions
What is the difference between adjustedCurves and missSBM?
Both compete on the same themes — r-package — within Infra & APIs. missSBM is currently shipping more aggressively (velocity 2.5 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 adjustedCurves better than missSBM?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. missSBM is currently shipping more aggressively (velocity 2.5 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 adjustedCurves?
Top adjustedCurves alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "adjustedCurves alternatives" section above for the current picks, or visit /alternatives/adjustedcurves for the full list with editorial commentary on each.
What are the best alternatives to missSBM?
Top missSBM alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "missSBM alternatives" section above for the current picks, or visit /alternatives/misssbm for the full list with editorial commentary on each.