fallback_subbars

To be used if subbars fails, usually when there is an argument to 
v_s ; adapted from lme4

internal

Estimates hierarchical models using mean-field variational Bayes.
At present, it can estimate logistic, linear, and negative binomial models.
It can accommodate models with an arbitrary number of random effects and
requires no integration to estimate. It also provides the ability to improve
the quality of the approximation using marginal augmentation.
Goplerud (2022) <doi:10.1214/21-BA1266> and Goplerud (2024) <doi:10.1017/S0003055423000035>
provide details on the variational algorithms.

Max Goplerud

vglmer

Variational Inference for Hierarchical Generalized Linear Models

fallback_subbars function

<dl><dt>term</dt>
<dd>a formula with lme4-style syntax; see <a href="/link/subbars?package=vglmer&version=1.0.5" data-mini-rdoc="vglmer::subbars">subbars</a></dd></dl>

Arguments

To be used if subbars fails, usually when there is an argument to 
v_s ; adapted from lme4 — fallback_subbars

<dl>

<dt>term</dt>
<dd>a formula with lme4-style syntax; see <a href='https://rdrr.io/pkg/lme4/man/subbars.html'>subbars</a></dd>

</dl>

fallback_subbars: To be used if subbars fails, usually when there is an argument to v_s ; adapted from lme4

Description

Usage

Arguments