DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.

Basic sensitivity analysis of the observed relative risks
    adjusting for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying
    Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021).

Exploring time series for signal detection. It is specifically designed
    to detect possible outbreaks using infectious disease surveillance data
    at the European Union / European Economic Area or country level.
    Automatic detection tools used are presented in the paper
    "Monitoring count time series in R: aberration detection in public health surveillance",
    by Salmon (2016) <doi:10.18637/jss.v070.i10>.
    The package includes:
    - Signal Detection tool, an interactive 'shiny' application
      in which the user can import external data and perform basic signal detection analyses;
    - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs.
      This report can also be stratified by population characteristics (see 'Population' variable).
    This project was funded by the European Centre for Disease Prevention and Control.

The 'EpiSimR' package provides an interactive 'shiny' app based on deterministic compartmental 
    mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. 
    It allows users to explore various intervention strategies, including vaccination and isolation, 
    by adjusting key epidemiological parameters. The methodology follows the approach described by 
    Brauer (2008) <doi:10.1007/978-3-540-78911-6_2>. Thanks to 'shiny' package.