Cellular cooperation compromises the established method of
	calculating clonogenic activity from limiting dilution assay (LDA) data.
	This tool provides functions that enable robust analysis in presence or
	absence of cellular cooperation. The implemented method incorporates the
	same cooperativity module to model the non-linearity associated with
	cellular cooperation as known from the colony formation assay (Brix et al.
	(2021) <doi:10.1038/s41596-021-00615-0>: "Analysis of clonogenic growth in
	vitro." Nature protocols).

Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.

Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.

Contains the development of a tool that provides a
    web-based graphical user interface (GUI) to perform a review of the
    scientific literature under the Bayesian approach of Latent Dirichlet
    Allocation (LDA)and machine learning algorithms. The application
    methodology is framed by the well known procedures in topic modelling
    on how to clean and process data. Contains methods described by 
    Blei, David M., Andrew Y. Ng, and Michael I. Jordan (2003) 
    <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf> 
    Allocation"; Thomas L. Griffiths and Mark Steyvers (2004) 
    <doi:10.1073/pnas.0307752101> ; Xiong Hui, et al (2019) 
    <doi:10.1016/j.cie.2019.06.010>.

A classification tree method that uses LDA (Linear
    Discriminant Analysis) for variable selection, split determination,
    and model fitting in terminal nodes.  It automatically handles missing
    values and offers visualization tools.