Assists actuaries and other insurance modellers in pricing,
    reserving and capital modelling for non-life insurance and
    reinsurance modelling. Provides functions that help model
    excess levels, capping and pure Incurred but not reported
    claims (pure IBNR).
    Includes capped mean, exposure curves and increased limit
    factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced
    LogNormal-Pareto and Sliced Gamma-Pareto distributions.
    Includes mean, probability density function (pdf), cumulative
    probability function (cdf) and inverse cumulative probability
    function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto
    distributions.
    Includes calculating pure IBNR exposure with LogNormal and
    Gamma distribution for reporting delay.
    Includes three shiny tools, one to simulate insurance claims applying
    reinsurance structures, fit generalised linear models and fit claims
    frequency or severity distributions.
    Methods used in the package refer to
    Free for All by Yiannis Parizas (2023) <https://www.theactuary.com/2023/03/02/free-all>;
    Escaping the triangle by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/06/2019/06/05/escaping-triangle>;
    Take to excess by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/03/2019/03/06/taken-excess>.

R interface for the 'netstat' command line utility used to retrieve and parse 
    commonly used network statistics, including available and in-use 
    transmission control protocol (TCP) ports. Primers offering technical background information 
    on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" 
    by Michael Kerrisk (2014) 
    <https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) 
    <https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.

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>.