Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.

Contains a number of common astronomy utility functions for cosmology and angular coordinates.

Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology.

Data from statistical agencies and other institutions often
    need to be protected before they can be published. This package 
    can be used to perturb statistical tables in a consistent way. The
    main idea is to add - at the micro data level - a record key for each
    unit. Based on these keys, for any cell in a statistical table a
    cell key is computed as a function on the record keys contributing to
    a specific cell. Values that are added to the cell in order to perturb
    it are derived from a lookup-table that maps values of cell keys to 
    specific perturbation values. The theoretical basis for the methods 
    implemented can be found in Thompson, Broadfoot and Elazar 
    (2013) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf>
    which was extended and enhanced by Giessing and Tent 
    (2019) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S2_Germany_Giessing_Tent_AD.pdf>.

Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products.

Annotate single-cell RNA sequencing data manually based on
    marker gene thresholds.
    Find cell type rules (gene+threshold) through exploration,
    use the popular piping operator '%>%' to reconstruct complex
    cell type hierarchies.
    'cellpypes' models technical noise to find positive and negative cells for
    a given expression threshold and returns cell type labels or pseudobulks.
    Cite this package as Frauhammer (2022) <doi:10.5281/zenodo.6555728> and
    visit <https://github.com/FelixTheStudent/cellpypes> for tutorials and newest
    features.