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Matt Moores

Matt Moores✓

Mastodon @mattmoores@fediscience.org
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📣Sponsored postOne disclosed post (toot) with your link and message. $12→ 🧵Sponsored threadA multi-post explainer or story — the fediverse rewards dept… $30→ 📦Product seeding / honest reviewSend me your product — I post a disclosed, honest take. Auth… $16→ 📌Pinned sponsored postA disclosed post pinned to the top of my profile for the mon… $36/mo→ 🔗Profile link (bio field)Your link in one of my profile metadata fields for the month… $18/mo→ 🔁Boost packageI boost (repost) your account's toots through the month — di… $24/mo→ 🏛️Instance / community sponsorshipSponsor the server I run — a sponsor line in the About/rules… $30/mo→ 🤝Mutual boost collabWe boost each other across instances — a value swap between … Swap→ 🎬UGC HireBranded content for your own channels & ads from $50→ 🎙️Podcast guest spotInvite Matt Moores onto your podcast (~45 min, remote) $26→

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Followers
187
Account age
3 yrs
🧰 Free analysis for Matt Moores
🕵️ Fake follower check 📊 Engagement rate 💰 What they charge

Known for

also thanks to @gaborcsardi @maelle for rhub v2, which makes it easy to configure GitHub Actions for crossplatform builds on Linux, macOS and Windows https://CRAN.R-project.org/package=rhub #rstats #statsodon #github #jira7 views also thanks to @gaborcsardi @maelle for rhub v2, which makes it easy to configure GitHub Actions for crossplatform builds on Linux, macOS and Windows https://CRAN.R-project.org/package=rhub #rstats #statsodon #github #jira version 0.7-0 of my R package `bayesImageS' is now available on CRAN for Linux and macOS (Windows binaries are still being built and should be available soon) The main change is a reduction in the console output for the exchange algorithm. There were also some minor changes to fix WARN and NOTE due to compatibility issues with the latest RcppArmadillo, which now uses the Armadillo 15 linear algeb4 views version 0.7-0 of my R package `bayesImageS' is now available on CRAN for Linux and macOS (Windows binaries are still being built and should be available soon) The main change is a reduction in the console output for the exchange algorithm. There were also some minor changes to fix WARN and NOTE due to compatibility issues with the latest RcppArmadillo, which now uses the Armadillo 15 linear algeb I mostly followed the 2nd edition of "R Packages" by @hadleywickham @jennybryan with a few minor tweaks to account for the differences between GitHub and Bitbucket/Jira https://r-pkgs.org/release.html I highly recommend this book for anyone who is writing or maintaining R code! #rstats #statsodon3 views I mostly followed the 2nd edition of "R Packages" by @hadleywickham @jennybryan with a few minor tweaks to account for the differences between GitHub and Bitbucket/Jira https://r-pkgs.org/release.html I highly recommend this book for anyone who is writing or maintaining R code! #rstats #statsodon 7529 is the minimum ESS for a 2 parameter model recommended by the R package mcmcse, for stable estimation with a relative tolerance of .05 The first 1000 iterations were discarded as burn-in.2 views 7529 is the minimum ESS for a 2 parameter model recommended by the R package mcmcse, for stable estimation with a relative tolerance of .05 The first 1000 iterations were discarded as burn-in.

📊 Post engagement

3
Avg engagement / post
1.6%
Engagement vs followers
Oct 2022
On Mastodon since

🔥 Top post: also thanks to @gaborcsardi @maelle for rhub v2, which makes it · 7 likes + reposts

📊 Activity & format

Posting cadence
0.37 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly images
Recent: 3 text · 9 image · 0 video.
Follower / following
0.6×
Follows 309 back. A more reciprocal / networked account.

Recent posts

View on Mastodon ↗
🔥 Top post also thanks to @gaborcsardi @maelle for rhub v2, which makes it easy to configure GitHub Actions for crossplatform builds on Linux, macOS and Windows https://CRAN.R-project.org/package=rhub #rstats #statsodon #github #jira ★ 7
all OK, you can stop panicking now 😅 it's never fun to stumble onto this... https://cran.r-project.org/web/checks/check_results_bayesImageS.html To all the R package maintainers out there who might be wondering why your build is currently broken on `r-devel-windows-x86_64` don't panic...hopefully this is temporary https://github.com/RcppCore/Rcpp/issues/1473 ★ 1 Success at last… To be fair, I can't say I wasn't warned: Note: This exercise is based on pre-release SDK software, which may be subject to change. Where necessary, we’ve used specific versions of packages; which may not reflect the latest available versio… "The Azure OpenAI SDK does not support Python 3.13 yet..." *facepalm" can I go back to R instead? I mostly followed the 2nd edition of "R Packages" by @hadleywickham @jennybryan with a few minor tweaks to account for the differences between GitHub and Bitbucket/Jira https://r-pkgs.org/release.html I highly recommend this book for anyon… ★ 3 version 0.7-0 of my R package `bayesImageS' is now available on CRAN for Linux and macOS (Windows binaries are still being built and should be available soon) The main change is a reduction in the console output for the exchange algorithm… ★ 4 7529 is the minimum ESS for a 2 parameter model recommended by the R package mcmcse, for stable estimation with a relative tolerance of .05 The first 1000 iterations were discarded as burn-in. ★ 2 This is the third in my series of examples using the exchange algorithm for Bayesian inference with intractable likelihoods. Here I fit an exponential random graph model (ERGM) to the karate club social network from Zachary (1977). Using t… ★ 1 the first 201 iterations were discarded as burn-in ★ 1

🐘 Community & instance

Home server
fediscience.org
Their home server on the fediverse — the instance a creator picks signals the community they belong to.
✅ Link-verified
Verified link
Proved ownership of a website linked on their profile — Mastodon's green-check verification, a real identity signal rather than a paid badge.
On Mastodon since
Oct 2022
Joined in the Twitter-exodus wave of late 2022 — part of the migration that made Mastodon a real destination.

💡 Facts

🗓️Joined Mastodon in 2022 — 3 years ago.
👁️Averages 3 views per post.
📤Posts about 0.4× per week.

🕵️ Fake follower check

Estimated
89/100
Excellent Credibility score
99%
Real Real audience
Low Fake-follower risk
High Data confidence
  • Est. 99% real, active audience · Low fake-follower risk.
  • Strong engagement (~1.6% of followers engage each post) — an active, real audience.
  • Verified account.
  • Established account (3+ years old).

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

he/him; researcher in computational Bayesian statistics. Author and maintainer of R packages bayesImageS & serrsBayes #rstats #Statsodon

📸 Gallery

all OK, you can stop panicking now 😅 it's never fun to stumble onto this... https://cran.r-project.org/web/checks/check_results_bayesImageS.html Success at last… also thanks to @gaborcsardi @maelle for rhub v2, which makes it easy to configure GitHub Actions for crossplatform builds on Linux, macOS and Windows https://CRAN.R-project.org/package=rhub #rstats #statsodon #github #jira I mostly followed the 2nd edition of "R Packages" by @hadleywickham @jennybryan with a few minor tweaks to account for the differences between GitHub and Bitbucket/Jira https://r-pkgs.org/release.html I highly recommend this book for anyone who is writing or maintaining R code! #rstats #statsodon version 0.7-0 of my R package `bayesImageS' is now available on CRAN for Linux and macOS (Windows binaries are still being built and should be available soon) The main change is a reduction in the console output for the exchange algorithm. There were also some minor changes to fix WARN and NOTE due to compatibility issues with the latest RcppArmadillo, which now uses the Armadillo 15 linear algeb 7529 is the minimum ESS for a 2 parameter model recommended by the R package mcmcse, for stable estimation with a relative tolerance of .05 The first 1000 iterations were discarded as burn-in. This is the third in my series of examples using the exchange algorithm for Bayesian inference with intractable likelihoods. Here I fit an exponential random graph model (ERGM) to the karate club social network from Zachary (1977). Using the R package Bergm, it took 2.9 hours for 131k iterations, resulting in an effective sample size (ESS) of 8750. https://www.kaggle.com/code/matttm/karate-club-n

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

Ingrid Glomp Ingrid Glomp 15% Mark McIntyre Mark McIntyre 15% Blackford Safe Routes Blackford Safe Routes 15% DenSmith Art DenSmith Art 15% Chancerubbage Chancerubbage 13% Bradley :smugcat: Bradley :smugcat: 12%

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