Cross-Entropy optimisation of unconstrained deterministic and noisy
    functions illustrated in Rubinstein and Kroese (2004, ISBN:
    978-1-4419-1940-3) through a highly flexible and customisable function which 
    allows user to define custom variable domains, sampling distributions,
    updating and smoothing rules, and stopping criteria. Several built-in
    methods and settings make the package very easy-to-use under standard
    optimisation problems.

Quantifies and removes technical noise from high-throughput 
        sequencing data. Two approaches are used, one based on the count
        matrix, and one using the alignment BAM files directly.
        Contains several options for every step of the process, as well
        as tools to quality check and assess the stability of output.

Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph 
    and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying 
    binary graph. This procedure comes with a control of the false discovery rate. The method is described
    in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, 
    E. Roquain, F. Villers (2020) <arXiv:1907.10176>.

Provides a suite of tools that can assist in enhancing the 
    processing efficiency of 'SQL' and 'R' scripts. 
    - The 'libr_unused()' retrieves a vector of package names that are called 
    within an 'R' script but are never actually used in the script.
    - The 'libr_used()' retrieves a vector of package names actively utilized 
    within an 'R' script; packages loaded using 'library()' but not actually 
    used in the script will not be included.
    - The 'libr_called()' retrieves a vector of all package names which are 
    called within an 'R' script.
    - 'nolock()' appends 'WITH (nolock)' to all tables in 'SQL' queries. This 
    facilitates reading from databases in scenarios where non-blocking reads are 
    preferable, such as in high-transaction environments.