Basedash
Basedash keeps pushing its data out of the workspace — now to people without accounts
A side-by-side editorial comparison of brglm2 and Holistics — release velocity, themes, recent moves, and the top alternatives to consider.
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
Holistics keeps fencing in the AI layer it spent the summer building.
Holistics ships small, frequent notes - often one or two sentences - across three strands: AI features in Explore and Chat, as-code control of presentation through AML, and workspace hygiene like file history and dark mode. The August entries are entirely about the AI layer's edges rather than its capabilities: an AI user attribute for restricting what the assistant can reach, and now redaction of the data it is allowed to see. Bodies are frequently a single line, so scope has to be read from the headline and the release-note URL.
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
The package's older work assumed the classical regime where observations comfortably outnumber parameters. mdyplFit() and its hd_correction argument target the opposite case, and the follow-up releases are almost entirely about it — Pearson residuals on original responses, aliased parameter handling, the sloe() signal-strength estimator ignoring leverage-one observations. Meanwhile the older surface gets graceful-failure work: brglm_fit() now returns its latest estimates with warnings rather than aborting.
Given that 1.0.1 and 1.1.0 are both dominated by mdyplFit follow-ups while the classical path receives only robustness fixes, further work on high-dimensional corrections is the likeliest direction.
Holistics ships small, frequent notes - often one or two sentences - across three strands: AI features in Explore and Chat, as-code control of presentation through AML, and workspace hygiene like file history and dark mode. The August entries are entirely about the AI layer's edges rather than its capabilities: an AI user attribute for restricting what the assistant can reach, and now redaction of the data it is allowed to see. Bodies are frequently a single line, so scope has to be read from the headline and the release-note URL.
The AI work has moved through a recognizable sequence: capability first with chart suggestions, then observability with AI Chat Insights for admins, then access control with an AI-specific user attribute, and now field-level redaction. Access control decides which rows the assistant can reach; redaction decides what it may see inside them - the same governance thread at finer grain. Alongside it, Holistics keeps pulling presentation into AML - custom charts, theme palettes, currency formats - so what analysts used to click is versioned as code.
With reach and visibility both now constrained, the remaining gap is accountability - logging what the assistant answered against which data - though the one-line release notes rarely signal scope far enough ahead to read the next step confidently.
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 brglm2 or Holistics.
Basedash keeps pushing its data out of the workspace — now to people without accounts
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See all brglm2 alternatives → · See all Holistics alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Holistics is currently shipping more aggressively (velocity 5.0 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. Holistics is currently shipping more aggressively (velocity 5.0 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 brglm2 alternatives in Analytics are ranked by recent ship velocity. Browse the "brglm2 alternatives" section above for the current picks, or visit /alternatives/brglm2 for the full list with editorial commentary on each.
Top Holistics alternatives in Analytics are ranked by recent ship velocity. Browse the "Holistics alternatives" section above for the current picks, or visit /alternatives/holistics for the full list with editorial commentary on each.