#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 · mastodon.social (Mastodon public tags API) · fetched 2026-09-18 17:04 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.social (Mastodon public search API) · fetched 2026-09-18 17:04 UTCLive pulse
measured · mastodon.social (Mastodon tag timeline) · fetched 2026-09-18 17:04 UTCEverything below is measured over the latest 16 public posts (spanning ~30143 hours).
Posting hours (UTC) — busiest: 17:00
Languages: English (16)
Avg boosts / post: 1.3
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!
#statstab #596 Generalized Additive Latent and Mixed Models {galamm} Thoughts: Complex ideas require complex (but appropriate) modelling strategies. #glmm #gam #r #rstats #lavaan #stan #latent #bayesian #bayes https://docs.ropensci.org/gala
Extremely nice review of REML estimation for generalized linear mixed models. Covers a couple of important papers that I was not aware of (e.g. Schall 1991 and Stiratelli 1984) until today, but also the work of Simon Wood in #rstats mgcv #G
What “glmm” means
WikipediaIn statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. They also inherit from generalized linear models the idea of extending linear mixed models to non-normal data.
“Generalized linear mixed model” on Wikipedia (CC BY-SA) →#glmm across platforms
every network with a public tag surfaceFollow #glmm straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
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