Coupling additive mean vector and covariance matrix modelling for multivariate Gaussian models is a complex task, requiring methodological choices on the model structure, scalability of the model fitting procedures, and a set of tailored inferential and model-checking tools. The SCM (Smoothing for Covariance matrix Modelling) R package enables smooth additive modelling of the elements of the mean vector and of an unconstrained parametrisation of the covariance matrix, while ensuring computational scalability by exploiting model sparsity and using the efficient linear algebra routines provided by the RcppArmadillo package. It also leverages the well-developed inferential methods and the visualization tools provided by the mgcv and mgcViz R packages.

In this talk, we will illustrate the modelling capabilities of the SCM package and we will provide useful insights into the data modelling process on several real-world applications. In particular, we will provide an overview of the main aspects of the model building and checking phases, as well as insights on how to interpret the model output. The SCM package is currently available at https://github.com/VinGioia90/SCM/.