A wrapper for Google's 'diff-match-patch' library. It provides basic tools
    for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.

Designed for network analysis, leveraging the personalized PageRank algorithm to calculate node scores in a given graph. This innovative approach allows users to uncover the importance of nodes based on a customized perspective, making it particularly useful in fields like bioinformatics, social network analysis, and more.

Generate a colorized diff of two R objects for an intuitive
    visualization of their differences.

An implementation of major general-purpose mechanisms for privatizing
    statistics, models, and machine learners, within the framework of differential
    privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms
    include the Laplace mechanism for releasing numeric aggregates, and the 
    exponential mechanism for releasing set elements. A sensitivity sampler 
    (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target 
    non-private function sensitivity; combined with the generic mechanisms, it 
    permits turn-key privatization of arbitrary programs.

An R interface to the 'codediff' JavaScript library (a copy of which is included in the package,
  see <https://github.com/danvk/codediff.js> for information).
  Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.

Enables the evaluation of diffusion distances for complex single-layer networks.
    Given a network one can define different types of Laplacian (or transition)
    matrices corresponding to different continuous-time random walks dynamics on the network. 
    This package enables the evaluation of Laplacians, stochastic matrices, and the 
    corresponding diffusion distance matrices. The metric structure induced by the network-driven
    process is richer and more robust than the one given by shortest-paths and allows to study 
    the geometry induced by different types of diffusion-like communication mechanisms taking 
    place on complex networks. 
    For more details see: De Domenico, M. (2017) <doi:10.1103/physrevlett.118.168301> and 
    Bertagnolli, G. and De Domenico, M. (2021) <doi:10.1103/PhysRevE.103.042301>.