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Comparison · Analytics

nmar vs washdata

A side-by-side editorial comparison of nmar and washdata — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:cran

nmar vs washdata: at a glance

Featurenmarwashdata
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themessurvey statistics, nonresponse, empirical likelihood, bootstrapopen data, wash surveys, data package, maintenance
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is nmar?

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.

Read the full nmar trajectory →

What is washdata?

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.

Read the full washdata trajectory →

nmar vs washdata: editorial side-by-side

N
nmar
ANALYTICS
0.0

NMAR landed on CRAN with two nonresponse estimators behind one interface, then started tuning it.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

Expect further engines under the same nmar() interface or wider bootstrap support, since the architecture was explicitly refactored to share structure across estimators.

W
washdata
ANALYTICS
0.0

washdata is a fixed survey dataset; eight years of releases have changed only its packaging.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

Expect further releases only when CRAN checks or infrastructure require them; there is no indication the survey data itself will be extended.

Alternatives to nmar and washdata

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.

See all nmar alternatives → · See all washdata alternatives →

Recent activity from nmar and washdata

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 6mo agonmarBootstrap backend now parallel-aware and configurable
  2. 6mo agowashdataRepository and toolchain maintenance
  3. 7mo agonmarCRAN submission fixes and DOI references
  4. 8mo agonmarNMAR 0.1.0
  5. 2y agowashdataDocumentation and formatting updates
  6. 5y agowashdataSecond release: documentation and formatting
  7. 8y agowashdataPre-release of the survey dataset
  8. 8y agowashdataFirst CRAN release of the Urban Water and Sanitation Survey

Frequently asked questions

What is the difference between nmar and washdata?

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.

Is nmar better than washdata?

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.

What are the best alternatives to nmar?

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.

What are the best alternatives to washdata?

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.