This tutorial introduces the estimation of partial correlation networks, a popular model for psychological data, using regularization techniques. It covers the interpretation of partial correlations, the application of LASSO regularization to limit spurious edges, and methods for handling non-normal data. The tutorial provides detailed R code for estimating these networks and demonstrates the method with an empirical example on post-traumatic stress disorder data. It also discusses the selection of hyperparameters, sample size determination, and the replicability of results. The tutorial emphasizes the importance of understanding the underlying assumptions and provides a checklist for common issues encountered when estimating regularized partial correlation networks.This tutorial introduces the estimation of partial correlation networks, a popular model for psychological data, using regularization techniques. It covers the interpretation of partial correlations, the application of LASSO regularization to limit spurious edges, and methods for handling non-normal data. The tutorial provides detailed R code for estimating these networks and demonstrates the method with an empirical example on post-traumatic stress disorder data. It also discusses the selection of hyperparameters, sample size determination, and the replicability of results. The tutorial emphasizes the importance of understanding the underlying assumptions and provides a checklist for common issues encountered when estimating regularized partial correlation networks.