This study compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations.

In this paper we present a new R package called BFpack that contains functions for Bayes factor hypothesis testing for the many common testing problems. The software includes novel tools for (i) Bayesian exploratory testing (e.g., zero vs positive vs negative effects), (ii) Bayesian confirmatory testing (competing hypotheses with equality and/or order constraints), (iii) common statistical analyses, such as linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, and random intercept models, (iv) using default priors, and (v) while allowing data to contain missing observations that are missing at random.

This research introduces a novel approach, using the Bayes factor, wherein a researcher can directly test for homogeneous within-person variance in hierarchical models. Additionally, we introduce a membership model that allows for classifying which (and how many) individuals belong to the common variance model.

Graphical modeling has emerged recently in psychology (Epskamp et al. 2018), where the data is typically long or low-dimensional (p < n; Donald R. Williams et al. (2019),
Donald R. Williams & Rast (2019)). The primary purpose of GGMnonreg is to provide methods that were specifically designed for low-dimensional data (e.g., those common in the
social-behavioral sciences).

A new software package, GGMncv, provides alternatives to graphical lasso (ℓ1-regularization) for study of complex relations in multivariate datasets.