Measurement Invariance Assessment Using Random Effects Models
and Shrinkage
Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.

Multiple Imputation Random Lasso for Variable Selection with
Missing Entries
Ying Liu, Yuanjia Wang, Yang Feng, Melanie M. Wall
Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) <doi:10.1214/15-AOAS899>. It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper.

Bayesian Model Search and Multilevel Inference for SNP
Association Studies
The functions in this package focus on intermediate
throughput case-control association studies, where the outcome
of interest is often a binary disease state and where the
genetic markers have been chosen to capture variation in a set
of related genes, such as those involved in a specific
biochemical pathway. Given this data, we are interested in
addressing two questions: "To what extent does the data support
an overall association between the pathway and outcome of
interest?" and "Which markers or genes are most likely to be
driving this association?" To address both of these
questions,this package performs a Bayesian model search
technique that utilizes Evolutionary Monte Carlo and searches
over models including main effects of all genetic markers and
marker-specific genetic effects in a computationally efficient
manner.  The package incorporates functions that perform a
marginal screen on the genetic markers, summarize the output of
the model search algorithm, including image plots of the models
with the highest posterior probability, marginal summaries of
SNP and gene inclusion probabilities and Bayes Factors, and
global summaries of the posterior probability and Bayes Factor
giving evidence of an association in the set of SNPs of
interest.