mixed-models-with-R VS bayesian

Compare mixed-models-with-R vs bayesian and see what are their differences.

mixed-models-with-R

Covers the basics of mixed models, mostly using @lme4 (by m-clark)
mixed-models R lme4 random-effects multilevel-models hierarchical-linear-models variance-components random-intercepts random-slopes covariance linear-mixed-models generalized-linear-mixed-models crossed-random-effects nested-random-effects nlme glmmtmb brms
Source Code
m-clark.github.io
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bayesian

Bindings for Bayesian TidyModels (by hsbadr)
Bayesian tidymodels brms Stan
Source Code
hsbadr.github.io
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mixed-models-with-R bayesian
1 1
120 42
- -
5.0 7.7
about 2 years ago 6 days ago
R R
- GNU General Public License v3.0 or later
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mixed-models-with-R

Posts with mentions or reviews of mixed-models-with-R. We have used some of these posts to build our list of alternatives and similar projects.

bayesian

Posts with mentions or reviews of bayesian. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-17.
  • How do you train a Bayesian model
    2 projects | /r/rstats | 17 Mar 2022
    There's MLMod and Bayesian which are TidyModels wrappers for stan_glm and brms (brms is my go to) but I haven't seen anything that indicates how to tune them.

What are some alternatives?

When comparing mixed-models-with-R and bayesian you can also consider the following projects:

rmarkdown - Dynamic Documents for R

multilevelmod - Parsnip wrappers for mixed-level and hierarchical models

brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

mixed-models-with-R vs rmarkdown bayesian vs multilevelmod bayesian vs brms