A powerful tool for automating the early detection of disease
             outbreaks in time series data. 'aeddo' employs advanced statistical 
             methods, including hierarchical models, in an innovative manner to 
             effectively characterize outbreak signals. It is particularly useful 
             for epidemiologists, public health professionals, and researchers 
             seeking to identify and respond to disease outbreaks in a timely 
             fashion. For a detailed reference on hierarchical models, consult 
             Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.

Package to incorporate change point analysis in ARIMA forecasting.

A powerful tool for automating the early detection of seasonal epidemic
             onsets in time series data. It offers the ability to estimate growth rates
             for consecutive time intervals and calculate the sum of cases (SoC) within 
             those intervals. It is particularly useful for epidemiologists, public 
             health professionals, and researchers seeking to identify and respond to 
             seasonal epidemics in a timely fashion.
             For reference on growth rate estimation, see Walling and Lipstich (2007) 
             <doi:10.1098/rspb.2006.3754> and Obadia et al. (2012) <doi:10.1186/1472-6947-12-147>.

We extend existing gene enrichment tests to perform adverse
    event enrichment analysis. Unlike the continuous gene expression data,
    adverse event data are counts. Therefore, adverse event data has many zeros
    and ties. We propose two enrichment tests. One is a modified Fisher's exact
    test based on pre-selected significant adverse events, while the other is
    based on a modified Kolmogorov-Smirnov statistic. We add Covariate
    adjustment to improve the analysis."Adverse event enrichment tests using
    VAERS" Shuoran Li, Lili Zhao (2020) <arXiv:2007.02266>.

Developed for Computing the probability density function, cumulative 
             distribution function, random generation, estimating the parameters of asymmetric
             exponential power distribution, and robust regression analysis with error
             term that follows asymmetric exponential power distribution. The asymmetric
             exponential power distribution studied here is a special case of that introduced
             by Dongming and Zinde-Walsh (2009) <doi:10.1016/j.jeconom.2008.09.038>.