Simulation models (apps) of various within-host immune response scenarios.
    The purpose of the package is to help individuals learn 
    about within-host infection and immune response modeling from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on what to do with the models.

Providing six different algorithms that can be used to split the 
    available data into training, test and validation subsets with similar distribution 
    for hydrological model developments. The dataSplit() function will help you divide 
    the data according to specific requirements, and you can refer to the par.default() 
    function to set the parameters for data splitting. The getAUC() function will help 
    you measure the similarity of distribution features between the data subsets.
    For more information about the data splitting algorithms, please refer to:
    Chen et al. (2022) <doi:10.1016/j.jhydrol.2022.128340>, 
    Zheng et al. (2022) <doi:10.1029/2021WR031818>.

Discretization-based random sampling algorithm that is useful for a complex model in high dimension is implemented. The normalizing constant of a target distribution is not needed. Posterior summaries are compared with those by 'OpenBUGS'. The method is described: Wang and Lee (2014) <doi:10.1016/j.csda.2013.06.011> and exercised in Lee (2009) <http://hdl.handle.net/1993/21352>.

This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mulè M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 <https://www.nature.com/articles/s41467-022-29356-8> for more details on the method.