Drafting an epidemiological report in 'Microsoft Word' format for a given disease,
  similar to the Annual Epidemiological Reports published by the European Centre 
  for Disease Prevention and Control. Through standalone functions, it is specifically 
  designed to generate each disease specific output presented in these reports and includes:
  - Table with the distribution of cases by Member State over the last five years;
  - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Trend plot with the trend and number of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level.
  Two types of datasets can be used:
  - The default dataset of dengue 2015-2019 data;
  - Any dataset specified as described in the vignette.

Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). <doi:10.1038/ejhg.2010.196>. 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011).  <doi:10.1093/bioinformatics/btr218>.)

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>.