#glmm
Live, measured metrics for the hashtag #glmm from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #glmm
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 13:59 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 13:59 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 13:59 UTCEverything below is measured over the latest 13 public posts (spanning ~26234 hours).
Posting hours (UTC) — busiest: 16:00
Languages: English (13)
Avg boosts / post: 1.4
Top of the latest posts
🚨New preprint on #deception detection analysis 🔍 We provide a tutorial on #Bayesian Mixed Effects Models for veracity data; no more aggregating & converting data to % 😤 (conflating acc w/ bias), just model the lie/truth answers directly!
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
#statstab #450 Fitting GAMs with brms Thoughts: Assuming linearity of your continuous predictors is not needed when you can add wiggles! #gam #glmm #linearmodel #modelling #brms #rstats #bayes #tutorial #splines #r https://fromthebottomofth
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/glmm