Features tools for the network data analysis and community detection. 
    Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. 
    Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models.
    Implements the following: 
    Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138>
    Bickel and Sarkar (2015) <doi:10.1111/rssb.12117>
    Lei (2016) <doi:10.1214/15-AOS1370>
    Wang and Bickel (2017) <doi:10.1214/16-AOS1457>
    Zhang and Amini (2020) <arXiv:2012.15047>
    Le and Levina (2022) <doi:10.1214/21-EJS1971>.

Enables users to retrieve data, meta-data, and codebooks from
    <https://nettskjema.no/>. The data from the API is richer than from
    the online data portal. This package is not developed
    by the University of Oslo IT. Mowinckel (2021) <doi:10.5281/zenodo.4745481>.

Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon 'igraph'.

Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.