#linearmixedmodels
Live, measured metrics for the hashtag #linearmixedmodels from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #linearmixedmodels
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.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 14:44 UTC0 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 2026-07-27 14:44 UTCNo related tags with measured usage found for #linearmixedmodels.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 14:44 UTCEverything below is measured over the latest 3 public posts (spanning ~27404 hours).
Posting hours (UTC)
Languages: German (2) · English (1)
Avg boosts / post: 6.3
Top of the latest posts
Anouncing our beautiful lab website: https://www.s-ccs.de Besides who we are, lab-philosphy, papers - we offer plenty of open #teaching materials (CC-BY)! Slides, Vectorgraphics, Syllabi, Demos and more - for topics from #Bayesian stats, #E
Due to a recent discussion with colleagues on whether and when to use #LinearMixedModels (#LMM), I wrote a blog post comparing LMM to other approaches using simulated data. I thought, it may also be useful for others working with hierarchic
One reason I switched to #juliaLang: I have seen speedups of 100x for fitting #LinearMixedModels #statistics x
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/linearmixedmodels