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.