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.

This R function implements the nonstationary Kriging model 
    proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> 
    for analyzing multi-fidelity computer outputs. This function computes 
    the maximum likelihood estimates for the model parameters as well 
    as the predictive means and variances of the exact solution.

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.

Various diffusion models to forecast new product growth. Currently
    the package contains Bass, Gompertz, Gamma/Shifted Gompertz and Weibull curves. See
    Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.