Missingness Alleviation for Network Analysis
Correlation Matrix Estimation with Support for Multiple Correlation Ty...
Dummy data sets for illustration purposes in the mantar package
Network Estimation via Neighborhood Selection using Information Criter...
Heuristic procedure for identifying ordered categorical variables
Stepwise Multiple Regression Model Search based on Information Criteri...
Regularized Network Estimation
Provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. Networks are estimated via neighborhood selection or regularization, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025a) <doi:10.31234/osf.io/qpj35> and Nehler and Schultze (2025b) <doi:10.1080/00273171.2025.2503833>. Deletion-based approaches are also available but play a secondary role.