#ordinalregression

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

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0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
2
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 2
0.5
Avg reactions / post
Mastodon · last 2

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 04:10 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 04:10 UTC

No related tags with measured usage found for #ordinalregression.

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 04:10 UTC

Everything below is measured over the latest 2 public posts (spanning ~1591 hours).

Posting hours (UTC)

00:0012:0023:00

Languages: Dutch (1) · English (1)

Avg boosts / post: 0

Top of the latest posts

  • @janfreyberg This tutorial on ordinal regression modeling may help: https://journals.sagepub.com/doi/full/10.1177/2515245918823199 (even though it doesn't use numpyro but #brms). #bayesian #ordinalregression

    Daniel Hoffmann 🌄@[email protected]♥ 1↻ 02022-11-12 08:13 UTCView post →

  • I want to use the #polr function of the #MASS package. What will be the factor of increase in computation time between a normal linear regression & f.e. a polr computation with the same input variables and an output with 15 levels? (Or even

    Wassermands@[email protected]♥ 0↻ 02023-01-17 15:28 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/ordinalregression