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

Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.

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.