tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of riem and xplainfi — release velocity, themes, recent moves, and the top alternatives to consider.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
riem pulls observations from the Iowa Environmental Mesonet's weather-station network. Its release history is two threads: successive rewrites of the HTTP and test-mocking stack, and a gradual tightening of function arguments that culminated in 1.0.0 removing convenient-but-dangerous defaults. Contributions come partly from IEM's own maintainer.
xplainfi treats feature importance as an estimate with error bars, not a number.
xplainfi implements feature importance methods for mlr3 — perturbation-based PFI, CFI and RFI, refit-based LOCO and WVIM, and SAGE. Its defining choice is that importance scores come with inference attached: several confidence-interval methods, including the Nadeau-Bengio correction and a distribution-free option added in 1.1.0. It declared itself released at 1.0.0 in January 2026.
riem pulls observations from the Iowa Environmental Mesonet's weather-station network. Its release history is two threads: successive rewrites of the HTTP and test-mocking stack, and a gradual tightening of function arguments that culminated in 1.0.0 removing convenient-but-dangerous defaults. Contributions come partly from IEM's own maintainer.
The HTTP thread has moved through httr to httr2, and mocking from vcr to httptest2 — following the broader rOpenSci HTTP-stack reorganisation rather than any need of its own. The API thread runs the other way: 1.0.0 removed defaults for date_start and station and flipped latlon to FALSE, trading convenience for callers being explicit about what they request. New arguments in the same release widened what a query can ask for.
With the API stabilised at 1.0.0 and the HTTP stack settled on httr2, the next release is more likely to expose additional IEM query parameters than to change plumbing again.
xplainfi implements feature importance methods for mlr3 — perturbation-based PFI, CFI and RFI, refit-based LOCO and WVIM, and SAGE. Its defining choice is that importance scores come with inference attached: several confidence-interval methods, including the Nadeau-Bengio correction and a distribution-free option added in 1.1.0. It declared itself released at 1.0.0 in January 2026.
Two lines of work run in parallel. The statistical side keeps adding inference options — variance corrections, conditional predictive impact, and the Lei et al. observation-wise loss-difference test — while the computational side attacks the cost of refit-based methods, most recently with a batch_size argument that parallelises refits and a default of one refit per resampling iteration. Support for pre-trained learners in 1.1.0 removes the refit requirement entirely in some workflows.
The stated reasoning that budget is better spent on resampling iterations than repeated refits suggests n_repeats may be removed from WVIM and LOCO outright, as the release notes hint.
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 riem or xplainfi.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
See all riem alternatives → · See all xplainfi alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. xplainfi 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. xplainfi 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 Analytics products to evaluate alongside.
Top riem alternatives in Analytics are ranked by recent ship velocity. Browse the "riem alternatives" section above for the current picks, or visit /alternatives/riem for the full list with editorial commentary on each.
Top xplainfi alternatives in Analytics are ranked by recent ship velocity. Browse the "xplainfi alternatives" section above for the current picks, or visit /alternatives/xplainfi for the full list with editorial commentary on each.