  We present vir, an R package for variational inference with shrinkage priors.
Our package implements variational and stochastic variational algorithms for
linear and probit regression models, the use of which is a common first step in
many applied analyses. We review variational inference and show how the
derivation for a Gibbs sampler can be easily modified to derive a corresponding
variational or stochastic variational algorithm. We provide simulations showing
that, at least for a normal linear model, variational inference can lead to
similar uncertainty quantification as the corresponding Gibbs samplers, while
estimating the model parameters at a fraction of the computational cost. Our
timing experiments show situations in which our algorithms converge faster than
the frequentist LASSO implementations in glmnet while simultaneously providing
superior parameter estimation and variable selection. Hence, our package can be
utilized to quickly explore different combinations of predictors in a linear
model, while providing accurate uncertainty quantification in many applied
situations. The package is implemented natively in R and RcppEigen, which has
the benefit of bypassing the substantial operating system specific overhead of
linking external libraries to work efficiently with R.
