Full text, in data frames containing one row per verse, of the 
    Standard Works of The Church of Jesus Christ of Latter-day Saints (LDS). 
    These are the Old Testament, (KJV), the New Testament (KJV), the Book of 
    Mormon, the Doctrine and Covenants, and the Pearl of Great Price.

A coalescent simulator that allows the rapid simulation of
    biological sequences under neutral models of evolution, see
    Staab et al. (2015) <doi:10.1093/bioinformatics/btu861>.
    Different to other coalescent based simulations,
    it has an optional approximation parameter that
    allows for high accuracy while maintaining a linear run time cost for long
    sequences. It is optimized for simulating massive data sets as produced by Next-
    Generation Sequencing technologies for up to several thousand sequences.

Performing single-cell imputation in a way that preserves the biological variations in the data. The package clusters the input data to do imputation for each cluster, and do a distribution check using the Anderson-Darling normality test to impute dropouts using mean or median (Yazici, B., & Yolacan, S. (2007) <DOI:10.1080/10629360600678310>).

A pipeline that can process single or multiple Single Cell RNAseq 
    samples primarily specializes in Clustering and Dimensionality Reduction. 
    Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, 
    Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte,
    Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling 
    them by biological names. Once users named each cluster, they can evaluate the 
    quality of them again and find the de novo marker genes also.

We integrated the common analysis methods utilized in single cell RNA sequencing data, which included cluster method, principal components analysis (PCA), the filter of differentially expressed genes, pathway enrichment analysis and correlated analysis methods.

'Last.fm'<https://www.last.fm> is a music platform focussed on building a 
    detailed profile of a users listening habits. It does this by 'scrobbling' (recording) 
    every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc)
    and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a 
    complete record of your entire listening history. 'scrobbler' provides helper functions
    to download and analyse your listening history in R.