Provides tools for sampling from a conditional copula density decomposed via 
     Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a 
     sampling are those which sample as first the conditioning variables (when following the 
     sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). 
     The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) 
     <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) 
     <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine 
     (based on information criteria) among those which allow for such a conditional sampling. 
     The package includes a function to compare scatterplot matrices and pair-dependencies of 
     two multivariate datasets.

Splits data into Gaussian type clusters using the Cross-Entropy 
    Clustering ('CEC') method. This method allows for the simultaneous use of 
    various types of Gaussian mixture models, for performing the reduction of 
    unnecessary clusters, and for discovering new clusters by splitting them. 
    'CEC' is based on the work of Spurek, P. and Tabor, J. (2014) 
    <doi:10.1016/j.patcog.2014.03.006>.

Provides analytical methods for analyzing CRISPR screen data
    at different levels of gene expression. Multi-component normal mixture models
    and EM algorithms are used for modeling.

Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.