tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
A side-by-side editorial comparison of dbt Core and enpls — release velocity, themes, recent moves, and the top alternatives to consider.
dbt-core spent a day backporting one deprecation warning across eight EOL branches — the message is: upgrade.
dbt-core maintains an unusually wide set of live branches, and on August 14 it cut releases for 1.1 through 1.8 in a single day. Every one of them carries the same single feature: a warning when the user is running a deprecated dbt version. The older branches picked up a few long-standing backports alongside it — semver comparison, JSON log formatting, seeds from stored manifest data — and 1.4 through 1.6 dropped Python 3.8 testing now that it is end of life.
enpls has not changed its statistics since 2016 — only its website, twice.
enpls implements ensemble partial least squares regression, with variants for feature selection, outlier detection and model applicability. Across the six most recent releases there is not one change to the modeling code. They cover a documentation website, a website URL change, a font stack, code indentation, a CI service, and most recently a GitHub Actions migration with an R CMD check note fix.
dbt-core maintains an unusually wide set of live branches, and on August 14 it cut releases for 1.1 through 1.8 in a single day. Every one of them carries the same single feature: a warning when the user is running a deprecated dbt version. The older branches picked up a few long-standing backports alongside it — semver comparison, JSON log formatting, seeds from stored manifest data — and 1.4 through 1.6 dropped Python 3.8 testing now that it is end of life.
This is a coordinated deprecation campaign rather than product work. Shipping the same warning to every ancient branch at once is how a maintainer starts reclaiming a support surface, and the parallel removal of Python 3.8 support points the same way. The actual development is happening on 1.11 and 1.12, where recent releases sync JSON schemas from dbt-fusion and fix adapter config recognition — the branch where the Fusion engine transition is visible.
Expect formal end-of-life announcements for the branches that just received the warning, and continued dbt-fusion schema convergence on 1.12. The backport waves should thin out once the deprecated branches are formally retired.
enpls implements ensemble partial least squares regression, with variants for feature selection, outlier detection and model applicability. Across the six most recent releases there is not one change to the modeling code. They cover a documentation website, a website URL change, a font stack, code indentation, a CI service, and most recently a GitHub Actions migration with an R CMD check note fix.
The statistical work finished around version 5.6, which added cross-validation fold control and fixed component selection when the maximum was left unspecified. Everything since has kept the package installable and its docs online. The 2025 release arriving the same day as sibling package grex, with the same two fixes, confirms the pattern: these are maintainer sweeps across a portfolio, not attention to enpls specifically.
The next release will almost certainly be another CRAN or tooling fix. Six consecutive infrastructure-only releases across nine years give no basis for expecting new methods.
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 dbt Core or enpls.
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
A single-purpose ggplot2 inset tool, refining the same three arguments.
An R symbolic-maths binding whose changelog is really the C++ core's release notes.
gtfstools stopped guarding its own object model and started accepting everyone else's.
The glue package that makes R carry units and uncertainty through the same calculation.
See all dbt Core alternatives → · See all enpls alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. dbt Core is currently shipping more aggressively (velocity 7.5 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. dbt Core is currently shipping more aggressively (velocity 7.5 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 dbt Core alternatives in Analytics are ranked by recent ship velocity. Browse the "dbt Core alternatives" section above for the current picks, or visit /alternatives/dbt-core for the full list with editorial commentary on each.
Top enpls alternatives in Analytics are ranked by recent ship velocity. Browse the "enpls alternatives" section above for the current picks, or visit /alternatives/enpls for the full list with editorial commentary on each.