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Comparison · Analytics
distributions3 vs PostHog
A side-by-side editorial comparison of distributions3 and PostHog — release velocity, themes, recent moves, and the top alternatives to consider.
distributions3 vs PostHog: at a glance
What is distributions3?
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
What is PostHog?
PostHog is filling in Logs and the mobile SDKs while quietly growing a support product.
PostHog ships weekly, and this week's batch is spread across four teams rather than concentrated in one product. Logs gets attribute filtering and a React Native capture path; the iOS SDK gains rage-click detection and a session-replay duration floor; Conversations picks up GitHub issues as an inbound support channel. Individually these are small, and the weekly digest bundles them into a single roundup entry.
distributions3 vs PostHog: editorial side-by-side
distributions3
6.3ANALYTICS
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
◆ Current state
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
◆ Where it's heading
Growth used to arrive as new distribution families contributed from outside - the extreme-value set, Erlang, later the Poisson binomial. This release changes the axis: alongside two new distributions it adds an inference layer (score, hessian) and a forecast-evaluation one (crps() methods against scoringRules), which are capabilities about distributions rather than more of them. Dependency weight is being cut at the same time, with ggplot2 demoted to Suggests and glue replaced by base R sprintf().
◆ Prediction
With numeric fallbacks and the derivative generics in place, expect analytic score() and hessian() methods to be filled in across more of the distribution catalogue. The constructor-default change is the likeliest source of follow-up fixes, since calls like Poisson() now return a length-zero distribution where they previously errored.
PostHog
0.0ANALYTICS
PostHog is filling in Logs and the mobile SDKs while quietly growing a support product.
◆ Current state
PostHog ships weekly, and this week's batch is spread across four teams rather than concentrated in one product. Logs gets attribute filtering and a React Native capture path; the iOS SDK gains rage-click detection and a session-replay duration floor; Conversations picks up GitHub issues as an inbound support channel. Individually these are small, and the weekly digest bundles them into a single roundup entry.
◆ Where it's heading
Two build-outs are running in parallel. The observability side — Logs plus the mobile SDKs — is being brought up to parity with what PostHog already offers on web, which is what makes the platform credible for mobile teams rather than web analytics with an SDK attached. The Conversations work is the more interesting thread: adding support channels moves PostHog past measuring users toward handling them, and it is being built out in the same incremental weekly rhythm as everything else.
◆ Prediction
Expect Logs and the mobile SDKs to keep receiving parity features on the weekly cadence, and expect Conversations to add further inbound channels beyond GitHub issues. The entries do not show whether Conversations is being positioned as a standalone product or a feature of the existing suite.
Alternatives to distributions3 and PostHog
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 distributions3 or PostHog.
RStudio
RStudio ships through release branches, and the notes are commit messages
dbt Core
dbt Fusion's second beta is adapter work: ClickHouse gets materializations, indexes, and catalogs
Fulcrum
Fulcrum is betting its whole map stack on Esri, with a hard Google Maps cutoff on September 1.
Holistics
Holistics keeps fencing in the AI layer it spent the summer building.
Dovetail
Dovetail spent July opening doors to other tools and August making its own rooms easier to enter.
See all distributions3 alternatives → · See all PostHog alternatives →
Recent activity from distributions3 and PostHog
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1h agodistributions3Empirical distributions, plus score and hessian generics ⚡
- 28d agodistributions3Maintenance moves to Achim Zeileis; moment calculations corrected
- 3mo agoPostHogWeekly roundup: Logs SQL editor, React Native log capture, iOS rage clicks
- 3mo agoPostHogGitHub issues as a support channel
- 3mo agoPostHogLog capture for React Native
- 3mo agoPostHogRage click support in the iOS SDK
- 3mo agoPostHogResend source for data warehouse
- 3mo agoPostHogAzure OpenAI support in LLM analytics
- 10mo agodistributions3ggplot2 compatibility for the plotting functions
- 1y agodistributions3Poisson binomial distribution, with a normal-approximation fallback
- 3y agodistributions3is_discrete and is_continuous generics, plus elementwise type-safety
- 4y agodistributions3Extreme-value family, Erlang, and a plotting generic
Frequently asked questions
What is the difference between distributions3 and PostHog?
They serve adjacent needs but don't currently overlap on shipped themes. distributions3 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.
Is distributions3 better than PostHog?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributions3 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.
What are the best alternatives to distributions3?
Top distributions3 alternatives in Analytics are ranked by recent ship velocity. Browse the "distributions3 alternatives" section above for the current picks, or visit /alternatives/distributions3-r for the full list with editorial commentary on each.
What are the best alternatives to PostHog?
Top PostHog alternatives in Analytics are ranked by recent ship velocity. Browse the "PostHog alternatives" section above for the current picks, or visit /alternatives/posthog for the full list with editorial commentary on each.