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
             across consecutive time intervals, calculate the sum of cases (SoC) within
             those intervals, and estimate seasonal onsets within user defined seasons.
             With use of a disease-specific threshold it also offers the possibility to
             estimate seasonal onset of epidemics.
             Additionally it offers the ability to estimate burden levels for seasons
             based on historical data. It is aimed towards epidemiologists,
             public health professionals, and researchers seeking to identify and respond
             to seasonal epidemics in a timely fashion.

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) <doi:10.48550/arXiv.2007.02266>.

Facilitate the analysis of data related to aquatic ecology, specifically the establishment of carbon budget. 
  Currently, the package allows the below analysis.
  (i) the calculation of greenhouse gas flux based on data obtained from trace gas analyzer using the method described in Lin et al. (2024).
  (ii) the calculation of Dissolved Oxygen (DO) metabolism based on data obtained from dissolved oxygen data logger using the method described in Staehr et al. (2010). 
  Yong et al. (2024) <doi:10.5194/bg-21-5247-2024>.
  Staehr et al. (2010) <doi:10.4319/lom.2010.8.0628>.