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>.

Supervised learning from a source distribution (with known segmentation into cell sub-populations) 
             to fit a target distribution with unknown segmentation. It relies regularized optimal transport to directly 
             estimate the different cell population proportions from a biological sample characterized with flow cytometry 
             measurements. It is based on the regularized Wasserstein metric to compare cytometry measurements from 
             different samples, thus accounting for possible mis-alignment of a given cell population across sample 
             (due to technical variability from the technology of measurements). Supervised learning technique based 
             on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a 
             mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2021) <arXiv:2006.09003>.