Updates on CRAN: amorem (1.2.0), codriver (1.1.0), distanceHD (1.4), future.batchtools (0.30.0), lmomco (2.5.8), modelbased (0.18.0), mpath (0.4-2.28), myman (0.12.0), progressr (1.1.0)
October 18, 2026 at 7:08 PM
Updates on CRAN: amorem (1.2.0), codriver (1.1.0), distanceHD (1.4), future.batchtools (0.30.0), lmomco (2.5.8), modelbased (0.18.0), mpath (0.4-2.28), myman (0.12.0), progressr (1.1.0)
Once your estimand is set, {modelbased} maps it directly:
• Quantity → means, contrasts, or slopes
• Unit → estimate = "average", "counterfactual", etc.
Check out the updated vignette on the modelisation-approach:
easystats.github.io/modelbased/a...
• Quantity → means, contrasts, or slopes
• Unit → estimate = "average", "counterfactual", etc.
Check out the updated vignette on the modelisation-approach:
easystats.github.io/modelbased/a...
The Modelisation Approach: An Analytical Workflow with Integrated Post-Estimation Framework
easystats.github.io
October 9, 2026 at 6:22 AM
Once your estimand is set, {modelbased} maps it directly:
• Quantity → means, contrasts, or slopes
• Unit → estimate = "average", "counterfactual", etc.
Check out the updated vignette on the modelisation-approach:
easystats.github.io/modelbased/a...
• Quantity → means, contrasts, or slopes
• Unit → estimate = "average", "counterfactual", etc.
Check out the updated vignette on the modelisation-approach:
easystats.github.io/modelbased/a...
What does a sound data analysis workflow look like? In a first step:
- Define your estimand (*quantity and unit of interest*) & research question
- Map reality to your model (choose variables wisely)
How the {modelbased}📦fits into this post-estimation workflow (second step): 👇
#rstats #easystats
- Define your estimand (*quantity and unit of interest*) & research question
- Map reality to your model (choose variables wisely)
How the {modelbased}📦fits into this post-estimation workflow (second step): 👇
#rstats #easystats
October 9, 2026 at 6:22 AM
What does a sound data analysis workflow look like? In a first step:
- Define your estimand (*quantity and unit of interest*) & research question
- Map reality to your model (choose variables wisely)
How the {modelbased}📦fits into this post-estimation workflow (second step): 👇
#rstats #easystats
- Define your estimand (*quantity and unit of interest*) & research question
- Map reality to your model (choose variables wisely)
How the {modelbased}📦fits into this post-estimation workflow (second step): 👇
#rstats #easystats
An estimand needs two things: your *quantity* and your *unit* (target population) of interest.
In {modelbased}, you control this via the `estimate` argument to say who is your target population:
• A specific individual (e.g. conditional)
• An average across individuals (marginal/population)
In {modelbased}, you control this via the `estimate` argument to say who is your target population:
• A specific individual (e.g. conditional)
• An average across individuals (marginal/population)
October 9, 2026 at 6:22 AM
An estimand needs two things: your *quantity* and your *unit* (target population) of interest.
In {modelbased}, you control this via the `estimate` argument to say who is your target population:
• A specific individual (e.g. conditional)
• An average across individuals (marginal/population)
In {modelbased}, you control this via the `estimate` argument to say who is your target population:
• A specific individual (e.g. conditional)
• An average across individuals (marginal/population)
Regression coefficients rarely give direct answers - "holding variables constant" is tricky. {modelbased} delivers your actual *quantity* of interest directly:
• Expected outcomes (predictions) → estimate_means()
• Group comparisons → estimate_contrasts()
• Effect sizes & trends → estimate_slopes()
• Expected outcomes (predictions) → estimate_means()
• Group comparisons → estimate_contrasts()
• Effect sizes & trends → estimate_slopes()
October 9, 2026 at 6:22 AM
Regression coefficients rarely give direct answers - "holding variables constant" is tricky. {modelbased} delivers your actual *quantity* of interest directly:
• Expected outcomes (predictions) → estimate_means()
• Group comparisons → estimate_contrasts()
• Effect sizes & trends → estimate_slopes()
• Expected outcomes (predictions) → estimate_means()
• Group comparisons → estimate_contrasts()
• Effect sizes & trends → estimate_slopes()
#statstab #631 Prior Information in Frequentist Research Designs: The Case of Neyman’s Sampling Theory
Thoughts: Model-based v design-based problem. This should be discussed more.
#modelbased #designbased #sampling #priors #neyman #frequentist #bayesian
link.springer.com/article/10.1...
Thoughts: Model-based v design-based problem. This should be discussed more.
#modelbased #designbased #sampling #priors #neyman #frequentist #bayesian
link.springer.com/article/10.1...
Prior Information in Frequentist Research Designs: The Case of Neyman’s Sampling Theory - Journal for General Philosophy of Science
We analyse the issue of using prior information in frequentist statistical inference. For that purpose, we scrutinise different kinds of sampling designs in Jerzy Neyman’s theory to reveal a variety o...
link.springer.com
October 5, 2026 at 8:16 PM
#statstab #631 Prior Information in Frequentist Research Designs: The Case of Neyman’s Sampling Theory
Thoughts: Model-based v design-based problem. This should be discussed more.
#modelbased #designbased #sampling #priors #neyman #frequentist #bayesian
link.springer.com/article/10.1...
Thoughts: Model-based v design-based problem. This should be discussed more.
#modelbased #designbased #sampling #priors #neyman #frequentist #bayesian
link.springer.com/article/10.1...
Fitted the model? Great, that was just step one. 🛠️
Learn how to extract the actual answers to your research questions (predictions, contrasts, slopes) using the {modelbased} modelisation-approach.
Read the new vignette here:
easystats.github.io/modelbased/a...
#RStats #easystats
Learn how to extract the actual answers to your research questions (predictions, contrasts, slopes) using the {modelbased} modelisation-approach.
Read the new vignette here:
easystats.github.io/modelbased/a...
#RStats #easystats
September 17, 2026 at 2:43 PM
Fitted the model? Great, that was just step one. 🛠️
Learn how to extract the actual answers to your research questions (predictions, contrasts, slopes) using the {modelbased} modelisation-approach.
Read the new vignette here:
easystats.github.io/modelbased/a...
#RStats #easystats
Learn how to extract the actual answers to your research questions (predictions, contrasts, slopes) using the {modelbased} modelisation-approach.
Read the new vignette here:
easystats.github.io/modelbased/a...
#RStats #easystats
Nice to hear! 🤩 If you're looking for some resources, we have slides here:
easystats.github.io/easystats/ar...
And of course lot's of working examples and detailed introductions here:
easystats.github.io/modelbased/
But I agree with Vincent, often, the "simple use cases" are sufficient for most.
easystats.github.io/easystats/ar...
And of course lot's of working examples and detailed introductions here:
easystats.github.io/modelbased/
But I agree with Vincent, often, the "simple use cases" are sufficient for most.
Learning resources
easystats.github.io
September 9, 2026 at 7:24 AM
Nice to hear! 🤩 If you're looking for some resources, we have slides here:
easystats.github.io/easystats/ar...
And of course lot's of working examples and detailed introductions here:
easystats.github.io/modelbased/
But I agree with Vincent, often, the "simple use cases" are sufficient for most.
easystats.github.io/easystats/ar...
And of course lot's of working examples and detailed introductions here:
easystats.github.io/modelbased/
But I agree with Vincent, often, the "simple use cases" are sufficient for most.
marginaleffects can do so much and the manual/book is amazing! It's basically replaced emmeans for me these days, now that I've figured out how it works. Might shift from teaching emmeans to easystats {modelbased} next year ... I think starting people out with marginaleffects might be a bit much
September 5, 2026 at 2:40 AM
marginaleffects can do so much and the manual/book is amazing! It's basically replaced emmeans for me these days, now that I've figured out how it works. Might shift from teaching emmeans to easystats {modelbased} next year ... I think starting people out with marginaleffects might be a bit much
Updates on CRAN: LogicForest (2.1.5), manynet (2.3.1), melodi (1.1.2), mikropml (1.7.1), mobdb (1.0.3), modelbased (0.17.0), modEvA (3.46), nparLD (2.3.1), performance (0.18.0), projoint (1.1.4), quickSentiment (0.3.6), RCtest (1.2), RMVL (1.1.0.4)
August 28, 2026 at 10:54 PM
Updates on CRAN: LogicForest (2.1.5), manynet (2.3.1), melodi (1.1.2), mikropml (1.7.1), mobdb (1.0.3), modelbased (0.17.0), modEvA (3.46), nparLD (2.3.1), performance (0.18.0), projoint (1.1.4), quickSentiment (0.3.6), RCtest (1.2), RMVL (1.1.0.4)
I was attending the 21st @eshms.bsky.social conference and beyond the many great sessions there was also an amazing session about the quantitative #MAIHDA approach. I updated the #rstats #easystats tutorial on MAIHDA modelling easystats.github.io/modelbased/a..., including some new things I learned.
Case Study: Intersectionality Analysis Using The MAIHDA Framework
easystats.github.io
August 22, 2026 at 10:04 AM
I was attending the 21st @eshms.bsky.social conference and beyond the many great sessions there was also an amazing session about the quantitative #MAIHDA approach. I updated the #rstats #easystats tutorial on MAIHDA modelling easystats.github.io/modelbased/a..., including some new things I learned.
Want to know what happens under the hood when calculating estimated marginal means? Read the {modelbased} vignette "Understanding marginalization methods"! It breaks down the different approaches with hands-on manual calculations using penguins-data.
easystats.github.io/modelbased/a...
easystats.github.io/modelbased/a...
Understanding marginalization methods
easystats.github.io
August 10, 2026 at 7:35 AM
Want to know what happens under the hood when calculating estimated marginal means? Read the {modelbased} vignette "Understanding marginalization methods"! It breaks down the different approaches with hands-on manual calculations using penguins-data.
easystats.github.io/modelbased/a...
easystats.github.io/modelbased/a...
And I'm kicking off this morning with a discussion in one channel about what y'all like to use to code up counterfactuals - marginaleffects, emmeans, broom:augment(), wrapping it all in modelbased, or what? Or will someone come up with some python wisdom...
August 27, 2025 at 2:25 PM
And I'm kicking off this morning with a discussion in one channel about what y'all like to use to code up counterfactuals - marginaleffects, emmeans, broom:augment(), wrapping it all in modelbased, or what? Or will someone come up with some python wisdom...
Modelbased for Quick and Beautiful Model Visualization in #rstats imachordata.com/2025/07/25/m... Thanks, @easystats.github.io!
Modelbased for Quick and Beautiful Model Visualization · I'm a Chordata! Urochordata!
imachordata.com
July 25, 2025 at 8:38 PM
Modelbased for Quick and Beautiful Model Visualization in #rstats imachordata.com/2025/07/25/m... Thanks, @easystats.github.io!
Very cool! Recently I started using easystats.github.io/modelbased/ for that as well - also really easy and a wide range of models and packages accommodated. It’s powerful times for multivariate modeling!
Estimation of Model-Based Predictions, Contrasts and Means
Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, s...
easystats.github.io
December 13, 2024 at 12:15 PM
Very cool! Recently I started using easystats.github.io/modelbased/ for that as well - also really easy and a wide range of models and packages accommodated. It’s powerful times for multivariate modeling!
Need to try this sometime to see how it compares to emmeans (what I’ve been using for the same purpose for years) and the viz I get from modelbased…
One thing that just keeps giving is @vincentab.bsky.social's marginaleffects package (see marginaleffects.com). It's just a complete game changer, moving from "trying to discern what model coefficients tell you after careful recoding" to "you know what, just compare these slopes, thx"
December 13, 2024 at 12:10 PM
Need to try this sometime to see how it compares to emmeans (what I’ve been using for the same purpose for years) and the viz I get from modelbased…
Not a critical issue but... I can't seem to update from modelbased .10 to modelbased .11 in the easystats package? Any one have any ideas? Seems like maybe version .10 is still the CRAN version even tho easystats.github.io/modelbased/ says otherwise? #rstats @easystats.bsky.social
May 2, 2025 at 1:28 PM
Not a critical issue but... I can't seem to update from modelbased .10 to modelbased .11 in the easystats package? Any one have any ideas? Seems like maybe version .10 is still the CRAN version even tho easystats.github.io/modelbased/ says otherwise? #rstats @easystats.bsky.social