Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
  multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
  the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
  data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
  validation techniques such as cross-validation to be applied to each set without concern for overfitting.

An efficient tool for fitting nested mixture models based on a shared set of 
    atoms via Markov Chain Monte Carlo and variational inference algorithms. 
    Specifically, the package implements the common atoms model (Denti et al., 2023), 
    its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite 
    model (D'Angelo and Denti, 2026). All models implement univariate nested mixtures 
    with Gaussian kernels equipped with a normal-inverse gamma prior distribution 
    on the parameters. Additional functions are provided to help analyze the 
    results of the fitting procedure.   
    References:       
    Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>,      
    D’Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>,      
    D’Angelo, Denti (2026) <doi:10.1214/24-BA1458>.

Data sets and code blocks for the book 'Statistical Analysis of 
  Network Data with R, 2nd Edition'.

A flexible framework for definition and application of time/depth-
    based rules for sets of parameters for single grains that can be used to 
    create artificial sediment profiles. Such profiles can be used for virtual 
    sample preparation and synthetic, for instance, luminescence measurements.

Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic 
             robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
	     covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
	     covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
	     clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
	     outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
	     applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
	     to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>,
	     Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.