Implementation of a probabilistic method for biclustering
    adapted to overdispersed count data. It is a Gamma-Poisson Latent
    Block Model.  It also implements two selection criteria in order to
    select the number of biclusters.

Qualitatively Constrained (Regression) Smoothing Splines via
        Linear Programming and Sparse Matrices.

Contains the R functions needed to perform Cluster-Of-Clusters Analysis (COCA)  and Consensus Clustering (CC). For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.

A 'shiny' app to discover cocktails. The
    app allows one to search for cocktails by ingredient,
    filter on rating, and number of ingredients. The
    package also contains data with the ingredients of
    nearly 26 thousand cocktails scraped from the web.

A copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2016). "Clustering dependent observations with copula functions". Statistical Papers, p.1-17. <doi:10.1007/s00362-016-0822-3>.

Useful tools for fitting, validating, and forecasting of practical convolution-closed time series models for low counts are provided. Marginal distributions of the data can be modeled via Poisson and Generalized Poisson innovations. Regression effects can be modelled via time varying innovation rates. The models are described in Jung and Tremayne (2011) <doi:10.1111/j.1467-9892.2010.00697.x> and the model assessment tools are presented in Czado et al. (2009) <doi:10.1111/j.1541-0420.2009.01191.x>, Gneiting and Raftery (2007) <doi:10.1198/016214506000001437> and, Tsay (1992) <doi:10.2307/2347612>.

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. 
  Sources of nonstationarity, such as trend, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. 
  An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.