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>.

Implements node subsampling methods for multivariate inference on
    network moments (rescaled motif counts), including: uniform node subsampling
    to approximate the joint distribution of multiple network moments (Algorithm 1);
    externally sparsified moments for density-matched comparisons (Algorithm 2);
    and a two-sample test for unmatchable networks with unequal edge densities
    via a split-and-sparsify subsampling procedure (Algorithm 3). Built-in support
    for V-shape (2-star), triangle, and 3-star motifs, with a user-extensible
    interface for arbitrary additional motifs. Parallel execution is supported
    via 'doParallel' and 'foreach'. Based on Qi, Hua, Li and Zhou (2024)
    <doi:10.48550/arXiv.2409.01599>.

Introduces a novel network-constrained survival
    analysis framework for variable selection and parameter estimation in
    penalized survival models with convex penalties. The package extends two
    classical survival models, the Cox Proportional Hazards (PH) model and the
    Accelerated Failure Time (AFT) model, by incorporating prior biological
    knowledge from curated interaction networks (e.g., KEGG) into a
    double-penalty framework. The first penalty enforces variable selection
    through a LASSO penalty, while the second preserves gene-gene correlations
    by incorporating Laplacian-based constraints, ensuring that biologically
    relevant network structures are maintained. Using censored survival data,
    the method enables the identification of predictive biomarkers and pathways
    with potential relevance for target therapies. Model estimation is
    performed via proximal optimization algorithms combined with
    cross-validation for reliable tuning. To enhance interpretability,
    dedicated utility functions are implemented to consolidate results,
    yielding biologically coherent insights that can support personalized
    medicine and contribute to improved patient outcomes.