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>.

Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'.  'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions.  For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.

Contains functions for statistical data analysis based on spatially-clustered techniques.
    The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models
    with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>.
    Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR),
    the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX).
    From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.