RMVMR
RMVMR is being tidied in lockstep with MVMR, the package it wraps
A side-by-side editorial comparison of Basedash and fasster — release velocity, themes, recent moves, and the top alternatives to consider.
Basedash is done answering questions about your data — it now wants to tell you what to do next.
Basedash spent July and early August building the surfaces of an AI-native BI tool: suggestions that propose questions before you type, subscriptions that push dashboards to Slack and email, audit logs that record every query the AI runs, and a developer platform exposing the whole feature set through an API. Tasks, now in research preview, changes the output shape entirely — instead of charts and answers it produces a ranked list of work with a stated rationale and expected outcome, then watches whether the metrics move. A sidebar rebuild the day before quietly names the product's five pillars: Chat, Dashboards, Automations, Insights, and Data.
fasster arrives as a fable-compatible state space model for switching seasonality.
fasster implements FASSTER, a state space model with a switching component in the measurement equation, aimed at series carrying several seasonal patterns and abrupt structural change. Version 0.2.0 is the first substantive release: a formula interface with trend(), season(), fourier(), ARMA() and xreg() plus the %S% switching and %?% conditional operators, and the full fable method set. Parameters come from a filtering-and-smoothing heuristic rather than full optimisation.
Basedash spent July and early August building the surfaces of an AI-native BI tool: suggestions that propose questions before you type, subscriptions that push dashboards to Slack and email, audit logs that record every query the AI runs, and a developer platform exposing the whole feature set through an API. Tasks, now in research preview, changes the output shape entirely — instead of charts and answers it produces a ranked list of work with a stated rationale and expected outcome, then watches whether the metrics move. A sidebar rebuild the day before quietly names the product's five pillars: Chat, Dashboards, Automations, Insights, and Data.
The arc runs from self-serve querying toward prescription and closed-loop measurement. Each release chips away at the assumption that a human must decide what to look at: suggestions removed the blank prompt, subscriptions removed the visit, and Tasks removes the interpretation step. The navigation rework is the tell that this is now a multi-module product rather than a chat box with extras — and the enterprise scaffolding arriving alongside it, audit logs covering AI queries plus retention controls, is what makes an autonomous analyst deployable rather than a demo.
Tasks graduating from research preview will be the release to watch; the outcome-tracking loop it describes only has value once it has run long enough to show whether its recommendations worked. Expect Tasks to become a sixth sidebar module and to be exposed through the developer platform API, since that is where every other Basedash capability has landed.
fasster implements FASSTER, a state space model with a switching component in the measurement equation, aimed at series carrying several seasonal patterns and abrupt structural change. Version 0.2.0 is the first substantive release: a formula interface with trend(), season(), fourier(), ARMA() and xreg() plus the %S% switching and %?% conditional operators, and the full fable method set. Parameters come from a filtering-and-smoothing heuristic rather than full optimisation.
The package sat at a 2018 development version for over seven years, so the news is that it exists as a usable model at all. Implementing the whole fable contract — forecast(), refit(), stream(), interpolate(), components() — means it slots into an existing forecasting workflow instead of asking for its own. The heuristic estimator is the open question these entries leave unanswered.
The obvious next step is supplementing the heuristic parameter estimates with proper optimisation, though the two entries here give no direct signal on timing.
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 Basedash or fasster.
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 Basedash alternatives → · See all fasster alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Basedash is currently shipping more aggressively (velocity 7.5 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. Basedash is currently shipping more aggressively (velocity 7.5 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 Basedash alternatives in Analytics are ranked by recent ship velocity. Browse the "Basedash alternatives" section above for the current picks, or visit /alternatives/basedash for the full list with editorial commentary on each.
Top fasster alternatives in Analytics are ranked by recent ship velocity. Browse the "fasster alternatives" section above for the current picks, or visit /alternatives/fasster for the full list with editorial commentary on each.