simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of healthyR.data and nmar — release velocity, themes, recent moves, and the top alternatives to consider.
From a bundled hospital dataset to a live CMS API client.
healthyR.data supplies the data layer for the healthyverse packages. It began by shipping hospital data inside the package and now fetches from CMS and provider endpoints at call time through get_cms_meta_data(), fetch_cms_data(), and their provider counterparts. The most recent release is a single httr2 compatibility fix.
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.
healthyR.data supplies the data layer for the healthyverse packages. It began by shipping hospital data inside the package and now fetches from CMS and provider endpoints at call time through get_cms_meta_data(), fetch_cms_data(), and their provider counterparts. The most recent release is a single httr2 compatibility fix.
The 2023 release added roughly twenty current_*_data() accessors, one per CMS measure file - a wide but static surface. The 2024 releases replaced that approach with metadata lookup plus generic fetchers, then taught the fetchers to handle CSV, Excel, and ZIP payloads rather than API responses alone. The package's weight has moved from what it ships to what it can retrieve.
With the fetch layer generalised, the next visible work is more likely record-limit and error handling around httr2 than further per-measure accessors.
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.
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 healthyR.data or nmar.
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
A raster-to-terra migration is the only readable change in a feed of merge notes.
A nycflights13 generator whose recent work is all about the data being right.
Conditional density and log-likelihood fill out a vine copula regression package.
A drop-in string API for base R, kept alive by upstream check failures.
See all healthyR.data alternatives → · See all nmar alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. healthyR.data and nmar 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. healthyR.data and nmar 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 healthyR.data alternatives in Analytics are ranked by recent ship velocity. Browse the "healthyR.data alternatives" section above for the current picks, or visit /alternatives/healthyr-data for the full list with editorial commentary on each.
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.