Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) <DOI:10.2307/2982890>).

Implements the Changes-in-Changes (CIC) estimator of Athey and
    Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> combined with
    synthetic control methods. Provides both the continuous CIC estimator
    (Theorem 3.1) and the discrete CIC estimator (Theorem 4.1) for
    integer-valued outcomes, with analytic and bootstrap inference.
    Also provides nonparametric estimation of the entire counterfactual
    distribution of outcomes for a treated group, allowing evaluation of
    average, quantile, and distributional treatment effects. Synthetic
    control weights are constructed via elastic net regularization to
    handle settings with many potential control units.

Provides wrappers for 'scclust', a C library for computationally efficient
    size-constrained clustering with near-optimal performance.
    See <https://github.com/fsavje/scclust> for more information.

Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.

The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added.

Various self-controlled case series models used to investigate 
        associations between time-varying exposures such as vaccines or
        other drugs or non drug exposures and an adverse event can be
        fitted. Detailed information on the self-controlled case series
        method and its extensions with more examples can be found in 
        Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y.
        (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies:
        A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) 
        and <https://sccs-studies.info/index.html>.