A package to estimate non-linear hierarchical models using the
variational algorithms described in Goplerud (2022) and in Goplerud (2024). It
also provides the option to improve an initial approximation using
marginally augmented variational Bayes (MAVB) also described in Goplerud (2022). It can be
installed from CRAN or the most-to-update version can be installed using
`devtools`

.

```
# CRAN
install.packages("vglmer")
# Up-to-Date GitHub Version
library(devtools)
devtools::install_github("mgoplerud/vglmer", dependencies = TRUE)
```

If you are interested in using partially factorized variational
inference (Goplerud, Papaspiliopoulos, and Zanella 2023), please switch
to the `collapsed`

branch and install that version of the
package. There are some important differences with this main branch,
especially in terms of some `vglmer_control`

naming
conventions. This branch will be eventually integrated into the main
package.

At present, `vglmer`

can fit logistic, linear, and
negative binomial outcomes with an arbitrary number of random effects.
Details on negative binomial inference can be found here
and are more experimental at the moment.

This package accepts “standard” glmer syntax of the form:

`vglmer(formula = y ~ x + (x | g), data = data, family = 'binomial')`

Splines can be estimated using `v_s(x)`

, similar to the
functionality in `mgcv`

, although with many fewer
options.

`vglmer(formula = y ~ v_s(x) + (x | g), data = data, family = 'binomial')`

Many standard methods from `lme4`

work,
e.g. `fixef`

, `coef`

, `vcov`

,
`ranef`

, `predict`

. Use `format_vglmer`

to parse all parameters into a single data.frame. Estimation can be
controlled via the numerous arguments to `control`

using
`vglmer_control`

. At the moment, Schemes I, II, and III in
Goplerud (2022) correspond to `strong`

, `partial`

,
and `weak`

. The default is `strong`

which
correspond to the strongest (worst) approximation. If the variance of
the parameters is of interest, then `weak`

will return better
results.

Please make an issue on GitHub with any concerns you have.