compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of hubPredEvalsData and libr — release velocity, themes, recent moves, and the top alternatives to consider.
The pipeline turning hub forecasts into dashboard-ready evaluation data.
hubPredEvalsData generates the scored evaluation data that hubverse prediction dashboards read, driven by a predevals-config.yml and scoring through hubEvals underneath. It is the youngest package in this part of the stack and the fastest-moving in configuration terms, having already passed a breaking 1.0.0 and a schema-versioned feature addition. Its output contract is a scores.csv file consumed downstream, which shapes what its releases care about.
The SAS datastep clone for R just got roughly nineteen times faster.
libr gives R users SAS-style data libraries and a datastep() construct, sitting alongside logr, reporter and procs in the r-sassy suite for analysts moving clinical workflows off SAS. Most of its release history is narrow bug-fixing in the libname() readers, particularly the sas7bdat engine. The exception dominates the window: a single 2026 release that rewrote datastep() performance and cut the installed package to a quarter of its former size.
hubPredEvalsData generates the scored evaluation data that hubverse prediction dashboards read, driven by a predevals-config.yml and scoring through hubEvals underneath. It is the youngest package in this part of the stack and the fastest-moving in configuration terms, having already passed a breaking 1.0.0 and a schema-versioned feature addition. Its output contract is a scores.csv file consumed downstream, which shapes what its releases care about.
Each release widens what the config file can express — round selection, then scale transformations with per-target overrides, then target labelling pulled from the hub's own task metadata. The pattern is consistent: capability that already exists in hubEvals gets a declarative surface here so hub maintainers configure it rather than write code. Recent attention to byte-stable output ordering shows the file is being treated as a reproducible artifact, not just a report.
Expect the config schema to keep absorbing hubEvals capabilities as declarative options, with continued attention to making scores.csv reproducible and diffable between runs.
libr gives R users SAS-style data libraries and a datastep() construct, sitting alongside logr, reporter and procs in the r-sassy suite for analysts moving clinical workflows off SAS. Most of its release history is narrow bug-fixing in the libname() readers, particularly the sas7bdat engine. The exception dominates the window: a single 2026 release that rewrote datastep() performance and cut the installed package to a quarter of its former size.
Two threads run through these entries — steady correctness work on SAS file import, and a much less frequent but far more consequential push on making datastep() viable at real data volumes. The recent fix to empty-variable typing suggests the sas7bdat reader is still where edge cases surface. Having addressed both speed and package size in one release, the obvious remaining pressure is correctness and coverage of SAS semantics rather than throughput.
Expect the next releases to continue narrowing sas7bdat import edge cases, with any further datastep() work aimed at supporting more SAS syntax rather than at speed.
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 hubPredEvalsData or libr.
An MLE package rebuilt around composable solvers, then renamed to match.
nabla dropped its C++ engine to chase exact derivatives at any order.
Eight months from first release to keyring caching and workload identity.
A research-project workflow package where the interesting work is in the plumbing.
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all hubPredEvalsData alternatives → · See all libr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. libr 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. libr 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 hubPredEvalsData alternatives in Analytics are ranked by recent ship velocity. Browse the "hubPredEvalsData alternatives" section above for the current picks, or visit /alternatives/hubpredevalsdata for the full list with editorial commentary on each.
Top libr alternatives in Analytics are ranked by recent ship velocity. Browse the "libr alternatives" section above for the current picks, or visit /alternatives/libr for the full list with editorial commentary on each.