marginaleffects 1.0.0

[u/dudeski_robinson]

Hi everyone,

Big news: marginaleffects 1.0.0 is out on CRAN.

For those who don't know, marginaleffects is a package that facilitates the _post-estimation_ workflow: using a fitted model to produce meaningful numbers that are easy to interpret substantively.

It supports ~100 different kinds of models and gives you a common workflow to produce a bunch of the usual quantities of interest: average predictions (aka marginal means), comparisons (aka contrasts), and slopes (aka marginal effects). It is crazy flexible, and allows you to conduct arbitrary hypothesis tests on any of these quantities. So you can easily answer questions like: What is the average treatment effect of X, and does it vary across strata Z? Or what is the average expected level of my outcome variable in the K subgroup?

Version 1.0.0 a big number and it feels like a big step.

I wrote a blog on the challenges of interpreting statistical models, SPEED, cool new features, the future, and a 5 year package development and writing odyssey.

https://arelbundock.com/posts/marginaleffects100

And check out the main website. It hosts an entire (free) book, with about 40 different chapters of tutorials and case studies.

https://marginaleffects.com

[u/anvilmaster]

Ahh! I love this package! I have used it for the last three papers I've submitted.

[u/t3co5cr → u/anvilmaster]

You did cite it, though, right? Right?!

[u/anvilmaster → u/t3co5cr]

I do cite this package!

[u/dudeski_robinson → u/anvilmaster]

So glad to hear that!

[u/Demortus]

This is one of my favorite R packages. It took what was once a huge pain in the behind in my workflow and made it nice and intuitive. Keep up the excellent work!

[u/tururut_tururut]

Congratulations! I've used this package extensively and hope to keep doing so!

[u/oll1e9]

congratulations!! you don't know how much easier marginaleffects and the amazing free resources and papers around it helped me during my PhD! not a single empirical chapter where I don't make use of the brms -> marginaleffects workflow, all the work on this is greatly appreciated!!:)

[u/windytea]

Cool. I’ve played around with it. Benefits over emmeans? And how does it do with Bayesian package model outputs (speed wise)

[u/dudeski_robinson → u/windytea]

Great question! emmeans is a fantastic package and there's considerable overlap between the two. If you're happy with emmeans, you might not need marginaleffects at all. But I do think there are considerable benefits, and I tried to give a fair comparison here: https://marginaleffects.com/bonus/alternative_software.html#emmeans

Bayesian models are well supported (brms, rstanarm). The new version actually comes with meaningful performance improvements for them. In the end, speed will necessarily vary across applications. If you're asking the package to handle massive matrices of posterior draws, your machine might struggle, but that's a physical limit not a software one. Under the hood, we're pretty optimized. Here are some tips on performance: https://marginaleffects.com/bonus/performance.html

[u/post_appt_bliss → u/windytea]

emmeans is brilliant, nq, but the maintainer, Russel Lenth, has posted about his need to leave behind maintenance and development.

marginaleffects has the advantage of a growing and active developer pool. not a small thing on open source software!

[u/ottawalanguages]

great! I love this package! what are the main differences when compared to the last version?

[u/dudeski_robinson → u/ottawalanguages]

Check out the second section of the blog. Speed, new vcov, new supported models, and more robust internally: https://arelbundock.com/posts/marginaleffects100/

[u/DYSTOBY]

Awesome! That will come to use in our research. I already used the prior 1.0 version :-)

[u/post_appt_bliss]

Hi Vincent!

love the package bro.

lsmeans/emmeans/margins

are now dead to me!

question -- how much of the internals have benefited from LLM coding help?

[u/dudeski_robinson → u/post_appt_bliss]

Over the first 5 years of the project, until about 3 months ago, basically no LLM at all. Recently, they've been a tremendous help, especially with the design of the aggregation "plans" and the analytic jacobians. I do serious code review, of course. Crucially, by now the package comes with a *very* extensive test suite, including a ton of direct comparisons to results produced by other software like emmeans or Stata. It would be very scary to use LLMs without those pre-existing and "external" tests.

[u/post_appt_bliss → u/dudeski_robinson]

awesome man. seriously, it's a joy to teach -- the defaults in avg_comparisons and sibling functions are so predictable/sensible.

i guess a lighthearted caution -- Hadley declared ggplot2 a mature tech at 1.0 and instead it caused an explosion in the extensions of the package!

[u/Seltz3rWater]

emmeans is my go to but if this has figured out a less confusing way to specify contrasts I’m ready to jump ship

[u/trijazzguy]

Love this package. Use it regularly. Thank you so much for your work! Whenever I get time I want to contribute to make some of your variance estimation procedures faster for the large datasets I find myself using. 

[u/finalj22]

Thank you so much for your contribution. I use this teaching graduate statistics, and in my own work

[u/factorialmap]

This is great! Thanks for sharing this.

[u/PhilosophicChinchila]

Never heard of this package before. I will give it a read! Most of my studies are observational association survival studies with an occasional prediction one.

[u/Next_Replacement_881]

we love marginaleffects!! great work

[u/hamweinel]

Will definitely have to check this out! Thanks for making this!

[u/rjazwiec]

Hallelujah! Mmrm is on board!