The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.

Tools to interface with Cytobank's API via R, organized by
    endpoints that represent various areas of Cytobank functionality. Learn more
    about Cytobank at <https://www.beckman.com/flow-cytometry/software>.

An implementation of Fan plots for cytometry data in 'ggplot2'. 
    For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart 
    <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).

Given the hypothesis of a bi-modal distribution of cells for
    each marker, the algorithm constructs a binary tree, the nodes of which are
    subpopulations of cells. At each node, observed cells and markers are modeled
    by both a family of normal distributions and a family of bi-modal normal mixture
    distributions. Splitting is done according to a normalized difference of AIC
    between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, 
    Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.

Typical morphological profiling datasets have millions of cells
    and hundreds of features per cell. When working with this data, you must
    clean the data, normalize the features to make them comparable across
    experiments, transform the features, select features based on their
    quality, and aggregate the single-cell data, if needed. 'cytominer' makes
    these steps fast and easy. Methods used in practice in the field are
    discussed in Caicedo (2017) <doi:10.1038/nmeth.4397>. An overview of the
    field is presented in Caicedo (2016) <doi:10.1016/j.copbio.2016.04.003>.

Provides comprehensive cytokine profiling analysis through quality control using biologically meaningful cutoffs on raw cytokine measurements and by testing for distributional symmetry to recommend appropriate transformations. Offers exploratory data analysis with summary statistics, enhanced boxplots, and barplots, along with univariate and multivariate analytical capabilities for in-depth cytokine profiling such as Principal Component Analysis based on Andrzej Maćkiewicz and Waldemar Ratajczak (1993) <doi:10.1016/0098-3004(93)90090-R>, Sparse Partial Least Squares Discriminant Analysis based on Lê Cao K-A, Boitard S, and Besse P (2011) <doi:10.1186/1471-2105-12-253>, Random Forest based on Breiman, L. (2001) <doi:10.1023/A:1010933404324>, and Extreme Gradient Boosting based on Tianqi Chen and Carlos Guestrin (2016) <doi:10.1145/2939672.2939785>.