#statsodon

Live, measured metrics for the hashtag #statsodon from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.

hashtag.org network · sponsored

Own #statsodon

This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.

$51.02/ year · 9-character #name
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0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
40
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 40
3.3
Avg reactions / post
Mastodon · last 40

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 03:31 UTC
0
07-22
0
07-23
0
07-24
0
07-25
0
07-26
0
07-27
0
07-28

0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.

Related hashtags

measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-28 03:31 UTC

No related tags with measured usage found for #statsodon.

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 03:31 UTC

Everything below is measured over the latest 40 public posts (spanning ~25349 hours).

Posting hours (UTC) — busiest: 20:00

00:0012:0023:00

Languages: English (40)

Avg boosts / post: 7.7

Top of the latest posts

  • New blog post! Seven (7!) new tidyexplain-esque animations showing how {dplyr}'s mutate(), summarize(), group_by(), and ungroup() all work together #rstats #statsodon https://www.andrewheiss.com/blog/2024/04/04/group_by-summarize-ungroup-an

    Andrew Heiss :rstats:@[email protected]♥ 19↻ 402024-04-04 21:17 UTCView post →

  • New post! If you think of "marginal effects" as slopes, the terms "marginal effects" and "conditional effects" aren't quite the same thing in the world of multilevel models, which is *so confusing*. I recreate a post by @kristoffer to show

    Andrew Heiss :rstats:@[email protected]♥ 15↻ 392022-11-30 03:04 UTCView post →

  • Check out this new ultimate guide to multilevel/hierarchical multinomial conjoint analysis with #rstats and {brms}, including how to find both marketing-style predicted market shares *and* polisci-style causal effects *across individual cov

    Andrew Heiss :rstats:@[email protected]♥ 8↻ 192023-08-12 16:47 UTCView post →

Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/statsodon