TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of nmar and washdata — release velocity, themes, recent moves, and the top alternatives to consider.
NMAR landed on CRAN with two nonresponse estimators behind one interface, then started tuning it.
Three releases in seven weeks, starting from nothing. The initial CRAN release implements empirical likelihood (Qin, Leung and Shao 2002) and both parametric and nonparametric exponential tilting (Riddles, Kim and Im 2016) for estimating means under nonignorable nonresponse, all reachable through a single nmar() call with formula syntax and direct support for survey.design objects. Since then the work has been operational: a configurable bootstrap backend and stricter input validation.
washdata is a fixed survey dataset; eight years of releases have changed only its packaging.
A data package distributing the Urban Water and Sanitation Survey, on CRAN since January 2018. No release has altered the data. The 2018 pair added survey country, year and aim to DESCRIPTION and fixed a README link; everything since — 2020, 2024 and the January 2026 release — is documentation, formatting, badges, repository refreshes and updates for a new rhub version.
Three releases in seven weeks, starting from nothing. The initial CRAN release implements empirical likelihood (Qin, Leung and Shao 2002) and both parametric and nonparametric exponential tilting (Riddles, Kim and Im 2016) for estimating means under nonignorable nonresponse, all reachable through a single nmar() call with formula syntax and direct support for survey.design objects. Since then the work has been operational: a configurable bootstrap backend and stricter input validation.
The package is positioning itself as the general interface to nonignorable-nonresponse estimation rather than a reference implementation of one paper — shared architecture across engines, one formula API, and integration with the survey package so weights and stratification come for free. The follow-up releases suggest the next constraint is compute: bootstrap variance estimation is the expensive part, and it now dispatches to future.apply when a parallel plan exists.
Expect further engines under the same nmar() interface or wider bootstrap support, since the architecture was explicitly refactored to share structure across estimators.
A data package distributing the Urban Water and Sanitation Survey, on CRAN since January 2018. No release has altered the data. The 2018 pair added survey country, year and aim to DESCRIPTION and fixed a README link; everything since — 2020, 2024 and the January 2026 release — is documentation, formatting, badges, repository refreshes and updates for a new rhub version.
Nothing is heading anywhere, and for a dataset package that is the point: the value is a citable, unchanging artifact, and the release history exists to keep it installable as R's toolchain moves. The maintenance cadence matches the maintainer's other nutrition packages, which received the same repository-refresh treatment in the same period. Note also that the tags are backfilled out of order — v0.1.0 carries a later stamp than v0.1.2.
Expect further releases only when CRAN checks or infrastructure require them; there is no indication the survey data itself will be extended.
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 nmar or washdata.
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
College football's open data client hit v2 — and now reports how many API calls you have left.
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
A spatial-statistics utility package exists to be depended on, and is built accordingly.
See all nmar alternatives → · See all washdata alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — cran — within Analytics. nmar and washdata 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. nmar and washdata 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.
Top nmar alternatives in Analytics are ranked by recent ship velocity. Browse the "nmar alternatives" section above for the current picks, or visit /alternatives/nmar for the full list with editorial commentary on each.
Top washdata alternatives in Analytics are ranked by recent ship velocity. Browse the "washdata alternatives" section above for the current picks, or visit /alternatives/washdata for the full list with editorial commentary on each.