RMVMR
RMVMR is being tidied in lockstep with MVMR, the package it wraps
A side-by-side editorial comparison of kernelshap and OpenObserve — release velocity, themes, recent moves, and the top alternatives to consider.
kernelshap makes permutation SHAP practical past eight features, then fixes the kernel weights it had wrong.
kernelshap computes model-agnostic SHAP values in R through Kernel SHAP, permutation SHAP and an exact additive explainer. Version 0.8.0 added a sampling permutation-SHAP algorithm with standard errors and early stopping, lifting the practical feature ceiling past what the exact method allows. Version 0.9.0 then corrected a bug in how kernel weights were computed — exact Kernel SHAP now agrees with exact permutation SHAP — and moved parallelism from foreach to doFuture.
After the 836-commit 0.92 release, OpenObserve is quietly moving its MCP server into the free tier
OpenObserve is in the settle-down phase after v0.92.0, the largest release the project has shipped, which added synthetic monitoring, Workflows v1, an expanded AI observability set, per-group and per-series alerting with SLOs, and moved Vortex and the MCP server into open source. The v0.92.1 patch that followed is small but pointed: the MCP Server setup page now renders on the OSS build, and an alerts bug where the HAVING clause was typed from the column rather than the aggregate is fixed. The 0.91 line continues to receive backported fixes in parallel.
kernelshap computes model-agnostic SHAP values in R through Kernel SHAP, permutation SHAP and an exact additive explainer. Version 0.8.0 added a sampling permutation-SHAP algorithm with standard errors and early stopping, lifting the practical feature ceiling past what the exact method allows. Version 0.9.0 then corrected a bug in how kernel weights were computed — exact Kernel SHAP now agrees with exact permutation SHAP — and moved parallelism from foreach to doFuture.
Two concerns drive this package: making exact methods reach further, and being demonstrably right. The first shows in the additive explainer, the optional background dataset and the sampling permutation algorithm; the second in unit tests written against Python's shap, credited fixes from outside contributors, and a willingness to ship a correctness fix that changes numbers people have already published. Speed work runs continuously underneath — direct solves replacing the Moore-Penrose pseudo-inverse, roughly 10% less memory.
The 0.6.0 and 0.7.0 notes each promised a stable 1.0.0 that has not arrived; with the weighting bug fixed and parallelism reworked, a 1.0 release is the most plausible next step.
OpenObserve is in the settle-down phase after v0.92.0, the largest release the project has shipped, which added synthetic monitoring, Workflows v1, an expanded AI observability set, per-group and per-series alerting with SLOs, and moved Vortex and the MCP server into open source. The v0.92.1 patch that followed is small but pointed: the MCP Server setup page now renders on the OSS build, and an alerts bug where the HAVING clause was typed from the column rather than the aggregate is fixed. The 0.91 line continues to receive backported fixes in parallel.
The MCP thread is the one to watch. Open-sourcing the server in 0.92.0 was the architectural move; serving its setup page on the OSS build a week later is what makes it reachable without an enterprise license. That points at agent clients as a first-class consumption path rather than an enterprise upsell, which is a different distribution bet than the synthetic-monitoring and Workflows surfaces that headlined the same release. Everything else in this window is stabilization — RC backports, memtable rotation, RBAC migrations — consistent with a project digesting a release that spanned two repositories and a large-scale crate reorganization.
Expect a run of 0.92.x patches concentrated on the three new surfaces, since synthetic monitoring, Workflows and eval scheduling all shipped at once with limited production exposure. The alerts fix suggests the aggregation path is a likely source of further corrections.
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 kernelshap or OpenObserve.
RMVMR is being tidied in lockstep with MVMR, the package it wraps
geoarrow tracks the GeoArrow spec and otherwise just keeps compiling
n2khab keeps retracting interpretations of habitat data it can't actually support
tidypolars is grinding toward complete dplyr coverage, one supported function at a time
OneSampleMR found that argument order in a formula was silently changing its estimates
bpbounds found the same swapped-cell bug twice and clamped its bounds back into range
See all kernelshap alternatives → · See all OpenObserve alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. OpenObserve is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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. OpenObserve is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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 kernelshap alternatives in Analytics are ranked by recent ship velocity. Browse the "kernelshap alternatives" section above for the current picks, or visit /alternatives/kernelshap for the full list with editorial commentary on each.
Top OpenObserve alternatives in Analytics are ranked by recent ship velocity. Browse the "OpenObserve alternatives" section above for the current picks, or visit /alternatives/openobserve for the full list with editorial commentary on each.