  The regsem package in R, an implementation of regularized structural equation
modeling (RegSEM; Jacobucci, Grimm, and McArdle 2016), was recently developed
with the goal of incorporating various forms of penalized likelihood estimation
in a broad array of structural equations models. The forms of regularization
include both the ridge (Hoerl and Kennard 1970) and the least absolute
shrinkage and selection operator (lasso; Tibshirani 1996), along with sparser
extensions. RegSEM is particularly useful for structural equation models that
have a small parameter to sample size ratio, as the addition of penalties can
reduce the complexity, thus reducing the bias of the parameter estimates. The
paper covers the algorithmic details and an overview of the use of regsem with
the application of both factor analysis and latent growth curve models.
