once again expressing my gratitude for the amazing R packages we have these days, my life would be so much harder without glmmTMB and marginaleffects
April 28, 2025 at 5:35 AM
once again expressing my gratitude for the amazing R packages we have these days, my life would be so much harder without glmmTMB and marginaleffects
After what feels like forever, I've gotten to do some statistical modelling and two package-related things stuck out to me:
{glmmTMB} makes parallel processing very easy
{marginaleffects} is SO useful! Plots of predicted probabilities seem much easier to explain than a table of odds ratios
{glmmTMB} makes parallel processing very easy
{marginaleffects} is SO useful! Plots of predicted probabilities seem much easier to explain than a table of odds ratios
September 30, 2026 at 1:55 AM
After what feels like forever, I've gotten to do some statistical modelling and two package-related things stuck out to me:
{glmmTMB} makes parallel processing very easy
{marginaleffects} is SO useful! Plots of predicted probabilities seem much easier to explain than a table of odds ratios
{glmmTMB} makes parallel processing very easy
{marginaleffects} is SO useful! Plots of predicted probabilities seem much easier to explain than a table of odds ratios
November 19, 2024 at 4:48 AM
I have just realised that {glmmTMB} has been downloaded far too rarely so far. Maybe people haven't recognized how flexible and strong this package became? Install it now, it's the {brms} in the frequentist world! cran.r-project.org/package=glmm... #rstats #glmmTMB (sorry for x-post, but it's x-mas)
December 8, 2023 at 9:15 AM
I have just realised that {glmmTMB} has been downloaded far too rarely so far. Maybe people haven't recognized how flexible and strong this package became? Install it now, it's the {brms} in the frequentist world! cran.r-project.org/package=glmm... #rstats #glmmTMB (sorry for x-post, but it's x-mas)
Around 3 years ago, we started writing papers during our weekly lab meetings. This is the first one from our new lab at
@ualberta.bsky.social
Location-Scale Models for Ecologists!
preprint: ecoevorxiv.org/repository/v...
tutorial: ayumi-495.github.io/Eco_location...
Enjoy! #glmmTMB #brms
@ualberta.bsky.social
Location-Scale Models for Ecologists!
preprint: ecoevorxiv.org/repository/v...
tutorial: ayumi-495.github.io/Eco_location...
Enjoy! #glmmTMB #brms
July 25, 2025 at 12:52 PM
Around 3 years ago, we started writing papers during our weekly lab meetings. This is the first one from our new lab at
@ualberta.bsky.social
Location-Scale Models for Ecologists!
preprint: ecoevorxiv.org/repository/v...
tutorial: ayumi-495.github.io/Eco_location...
Enjoy! #glmmTMB #brms
@ualberta.bsky.social
Location-Scale Models for Ecologists!
preprint: ecoevorxiv.org/repository/v...
tutorial: ayumi-495.github.io/Eco_location...
Enjoy! #glmmTMB #brms
We are pleased to announce that Mikael Jagan has joined the R Core Team. #RStats
He has been an active contributor to the R project for several years, proposing bug fixes and enhancements. Further, he has been co-authoring/maintaining the important Matrix package and more recently lme4 and glmmTMB.
He has been an active contributor to the R project for several years, proposing bug fixes and enhancements. Further, he has been co-authoring/maintaining the important Matrix package and more recently lme4 and glmmTMB.
May 7, 2026 at 10:28 AM
We are pleased to announce that Mikael Jagan has joined the R Core Team. #RStats
He has been an active contributor to the R project for several years, proposing bug fixes and enhancements. Further, he has been co-authoring/maintaining the important Matrix package and more recently lme4 and glmmTMB.
He has been an active contributor to the R project for several years, proposing bug fixes and enhancements. Further, he has been co-authoring/maintaining the important Matrix package and more recently lme4 and glmmTMB.
Happy to see that ordered beta regression reached 100 citations on Google Scholar!
The model has citations from work in climate science, ecology, medicine, psychology, & political science, just to name a few.
Thanks to all of you for using ordbetareg (or glmmTMB)!!
#rstats
The model has citations from work in climate science, ecology, medicine, psychology, & political science, just to name a few.
Thanks to all of you for using ordbetareg (or glmmTMB)!!
#rstats
March 25, 2025 at 1:35 AM
Happy to see that ordered beta regression reached 100 citations on Google Scholar!
The model has citations from work in climate science, ecology, medicine, psychology, & political science, just to name a few.
Thanks to all of you for using ordbetareg (or glmmTMB)!!
#rstats
The model has citations from work in climate science, ecology, medicine, psychology, & political science, just to name a few.
Thanks to all of you for using ordbetareg (or glmmTMB)!!
#rstats
Student-t. Implemented in glmmTMB
September 14, 2025 at 6:43 PM
Student-t. Implemented in glmmTMB
seems glmmTMB has been listening to that new Kendrick
December 3, 2024 at 5:27 PM
seems glmmTMB has been listening to that new Kendrick
Fast phylogenetic generalised linear mixed-effects modelling using the glmmTMB R package https://www.biorxiv.org/content/10.64898/2025.12.20.695312v1
December 23, 2025 at 8:31 PM
Fast phylogenetic generalised linear mixed-effects modelling using the glmmTMB R package https://www.biorxiv.org/content/10.64898/2025.12.20.695312v1
How to deal with microbiome data that is so dependent on the precise location in the field? Use some fancy bioinformatics tools:
github.com/glmmTMB/glmm...
github.com/glmmTMB/glmm...
GitHub - glmmTMB/glmmTMB: glmmTMB
glmmTMB. Contribute to glmmTMB/glmmTMB development by creating an account on GitHub.
github.com
November 6, 2025 at 3:56 PM
How to deal with microbiome data that is so dependent on the precise location in the field? Use some fancy bioinformatics tools:
github.com/glmmTMB/glmm...
github.com/glmmTMB/glmm...
Going to the ordinal package (for modelling discrete scale data, i.e. likert) is painful after glmmtmb. Is there any other better option rather than go into brms?
December 13, 2024 at 9:08 AM
Going to the ordinal package (for modelling discrete scale data, i.e. likert) is painful after glmmtmb. Is there any other better option rather than go into brms?
In fairness, Kasper Kristensen = lead glmmTMB author/architect of TMB; Mollie Brooks = instigator & maintainer - both Danish/working in Denmark 🇩🇰: also Anders Nielsen 🇩🇰, Martin Mächler 🇨🇭, Arni Magnusson 🇮🇸, Hans Skaug 🇧🇻, Casper Berg 🇩🇰, Koen van Bentham 🇳🇱, Maeve McGillycuddy 🇦🇺
April 28, 2025 at 1:22 PM
In fairness, Kasper Kristensen = lead glmmTMB author/architect of TMB; Mollie Brooks = instigator & maintainer - both Danish/working in Denmark 🇩🇰: also Anders Nielsen 🇩🇰, Martin Mächler 🇨🇭, Arni Magnusson 🇮🇸, Hans Skaug 🇧🇻, Casper Berg 🇩🇰, Koen van Bentham 🇳🇱, Maeve McGillycuddy 🇦🇺
Updates on CRAN: glmmTMB (1.1.15.2), HotellingEllipse (1.3.0), jumble (0.2.0), plug (0.2.0), quallmer (0.5.0), renv (1.3.0), rjd3workspace (3.9.0), rxode2 (5.1.7.1), SigBridgeRUtils (0.2.7), weatherOz (3.0.1), xplaineff (0.1.1)
September 29, 2026 at 3:24 PM
Updates on CRAN: glmmTMB (1.1.15.2), HotellingEllipse (1.3.0), jumble (0.2.0), plug (0.2.0), quallmer (0.5.0), renv (1.3.0), rjd3workspace (3.9.0), rxode2 (5.1.7.1), SigBridgeRUtils (0.2.7), weatherOz (3.0.1), xplaineff (0.1.1)
surprised that nobody has made a mice.impute function that uses glmmTMB() for multilevel data. glmer() is old, slow, and often errors out
August 15, 2025 at 5:43 AM
surprised that nobody has made a mice.impute function that uses glmmTMB() for multilevel data. glmer() is old, slow, and often errors out
🚀 New #rstats 📦: ecoXCorr now available on CRAN!
It provides a simple workflow to explore lagged associations between environmental #timeseries and eco / epidemio responses:
➡️ flexible lag intervals
➡️ GLMM via glmmTMB
➡️ plot cross-correlation maps
see github.com/Nmoiroux/eco...
🌐🧪🌍
#ecology
It provides a simple workflow to explore lagged associations between environmental #timeseries and eco / epidemio responses:
➡️ flexible lag intervals
➡️ GLMM via glmmTMB
➡️ plot cross-correlation maps
see github.com/Nmoiroux/eco...
🌐🧪🌍
#ecology
New CRAN package ecoXCorr with initial version 0.1.9
#rstats
https://cran.r-project.org/package=ecoXCorr
#rstats
https://cran.r-project.org/package=ecoXCorr
CRAN: Package ecoXCorr
Tools for analysing lagged relationships between environmental variables and ecological or epidemiological time series. The package implements a workflow to aggregate meteorological data over multiple lagged intervals, fit regression models, including mixed-effect models using 'glmmTMB', for each lag window, and visualise varied models outcomes (effect strength and direction, model prediction error...) using cross-correlation maps ('CCM').
cran.r-project.org
April 3, 2026 at 10:40 AM
🚀 New #rstats 📦: ecoXCorr now available on CRAN!
It provides a simple workflow to explore lagged associations between environmental #timeseries and eco / epidemio responses:
➡️ flexible lag intervals
➡️ GLMM via glmmTMB
➡️ plot cross-correlation maps
see github.com/Nmoiroux/eco...
🌐🧪🌍
#ecology
It provides a simple workflow to explore lagged associations between environmental #timeseries and eco / epidemio responses:
➡️ flexible lag intervals
➡️ GLMM via glmmTMB
➡️ plot cross-correlation maps
see github.com/Nmoiroux/eco...
🌐🧪🌍
#ecology
Are you lumping together convergence failures (A [calc.derivs=FALSE], B, C) and singularity issues (A [complex random intercepts], F)? Are you considering the (new to lme4, available for a while in glmmTMB) diag()/cs()/ar1() options, or factor-analytic/reduced-rank (rr() in glmmTMB) models?
March 30, 2026 at 2:16 PM
Are you lumping together convergence failures (A [calc.derivs=FALSE], B, C) and singularity issues (A [complex random intercepts], F)? Are you considering the (new to lme4, available for a while in glmmTMB) diag()/cs()/ar1() options, or factor-analytic/reduced-rank (rr() in glmmTMB) models?
I made Conway–Maxwell–Binomial regression on #TMB + #glmmTMB. A bounded-count family that handles both over- and under-dispersion through one parameter.. Demo on coral fertilization data from @benoitpujol.bsky.social #rstats #STEM #biology
jbogomolovas2.github.io/Julius-s-Blo...
jbogomolovas2.github.io/Julius-s-Blo...
Conway–Maxwell–Binomial regression: two-directional dispersion for bounded counts – Julius’s Blog
Data Science, Swimming Analytics, and R Programming
jbogomolovas2.github.io
May 25, 2026 at 6:44 AM
I made Conway–Maxwell–Binomial regression on #TMB + #glmmTMB. A bounded-count family that handles both over- and under-dispersion through one parameter.. Demo on coral fertilization data from @benoitpujol.bsky.social #rstats #STEM #biology
jbogomolovas2.github.io/Julius-s-Blo...
jbogomolovas2.github.io/Julius-s-Blo...
TIL that glmmTMB can fit phylogenetic generalized mixed models. is there anything this package can't do??
August 21, 2026 at 4:16 PM
TIL that glmmTMB can fit phylogenetic generalized mixed models. is there anything this package can't do??
April 8, 2026 at 12:11 AM
Beside the packages I'm (co-) developing, I most often use/load ggplot2, glmmTMB and brms. Important packages that are used by my packages are e.g. haven, gt or marginaleffects. That really covers 90% of my work, I think?
July 18, 2025 at 9:44 PM
Beside the packages I'm (co-) developing, I most often use/load ggplot2, glmmTMB and brms. Important packages that are used by my packages are e.g. haven, gt or marginaleffects. That really covers 90% of my work, I think?
Help! I have a lumpy posterior!
Also my models look funny.
We ran imputation with mice in R, and for the two variables involved in this model, everything looked good (psrf < 1.05, chains mixed, etc.)
But the intercept estimates and SEs vary widely across imputations.
Isn't that concerning?
Also my models look funny.
We ran imputation with mice in R, and for the two variables involved in this model, everything looked good (psrf < 1.05, chains mixed, etc.)
But the intercept estimates and SEs vary widely across imputations.
Isn't that concerning?
March 11, 2025 at 2:00 PM
Help! I have a lumpy posterior!
Also my models look funny.
We ran imputation with mice in R, and for the two variables involved in this model, everything looked good (psrf < 1.05, chains mixed, etc.)
But the intercept estimates and SEs vary widely across imputations.
Isn't that concerning?
Also my models look funny.
We ran imputation with mice in R, and for the two variables involved in this model, everything looked good (psrf < 1.05, chains mixed, etc.)
But the intercept estimates and SEs vary widely across imputations.
Isn't that concerning?
Best thing is you can use priors in glmmTMB, so often you can address convergence and singularity issues easily
March 6, 2025 at 7:12 AM
Best thing is you can use priors in glmmTMB, so often you can address convergence and singularity issues easily
