Analysis and visualization of cellular energetics data from
    Agilent Seahorse XF96. Cellular energetics is how cells make, use, and
    distribute units of energy (primarily ATP). Measuring real-time cellular
    energetics is essential to understanding a tissue or cell’s bioenergetic
    state and fuel dependencies. The Seahorse machine measures a cell’s or
    matrix’s oxygen consumption rate (OCR) – a proxy of oxidative
    phosphorylation – and extracellular acidification rate – a proxy of
    glycolysis. This package offers flexible and fast analysis and plotting
    capabilities for such data using the methods described by Mookerjee et al.
    (2017) <doi:10.1074/jbc.m116.774471>.

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>.

Create and learn Chain Event Graph (CEG) models using a Bayesian 
    framework. It provides us with a Hierarchical Agglomerative algorithm to 
    search the CEG model space.
    The package also includes several facilities for visualisations of the
    objects associated with a CEG. The CEG class can represent a range of
    relational data types, and supports arbitrary vertex, edge and graph
    attributes. A Chain Event Graph is a tree-based graphical model that
    provides a powerful graphical interface through which domain experts can
    easily translate a process into sequences of observed events using plain
    language. CEGs have been a useful class of graphical model especially to
    capture context-specific conditional independences. References: Collazo R, 
    Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018
    (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. 
    The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169
    <doi:10.1214/15-EJS1068>.