Retrieves code comment decorations for C++
    languages of the form '\\ [[xyz]]', which are used for automated
    wrapping of C++ functions.

DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
    of Artificial Training Examples) builds an ensemble of J48 trees by recursively
    adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) <DOI:10.1016/j.inffus.2004.04.001>").

A decorator is a function that receives a function, extends
    its behaviour, and returned the altered function. Any caller that uses
    the decorated function uses the same interface as it were the
    original, undecorated function. Decorators serve two primary uses: (1)
    Enhancing the response of a function as it sends data to a second
    component; (2) Supporting multiple optional behaviours. An example of
    the first use is a timer decorator that runs a function, outputs its
    execution time on the console, and returns the original function's
    result. An example of the second use is input type validation
    decorator that during running time tests whether the caller has passed
    input arguments of a particular class.  Decorators can reduce
    execution time, say by memoization, or reduce bugs by adding defensive
    programming routines.

Data whitening is a widely used preprocessing step to remove correlation structure since statistical models often assume independence. Here we use a probabilistic model of the observed data to apply a whitening transformation. This Gaussian Inverse Wishart Empirical Bayes model substantially reduces computational complexity, and regularizes the eigen-values of the sample covariance matrix to improve out-of-sample performance.

Provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point.