Title: Ensemble Based Machine Learning Approach for Predicting
Authors@R: c(person(" Dipro", "Sinha", role = c("aut","cre"), email = "diprosinha@gmail.com"),
person("Sunil", "Archak", role = "aut", email = "sunil.archak@gmail.com"),
person("Dwijesh Chandra", "Mishra", role = "aut", email = "dwij.mishra@gmail.com"),
person("Tanwy", "Dasmandal", role = "aut", email = "tanwydasmandal20@gmail.com"),
person("Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
Description: 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>.
Imports: stats, devtools, tidyverse, seqinr, Biostrings,
splitstackshape, entropy, party, stringr, tibble, doParallel,
parallel, e1071, caret, randomForest, gbm, foreach, ftrCOOL,

role = c("aut", "fnd"), email = "joana.gomes.dias@ecdc.europa.eu", comment = "Project manager"),
Imports: dplyr, graphics, ISOweek, rmarkdown, shiny, surveillance,
Suggests: DT, ggplot2, knitr (>= 1.20), pander
Joana Gomes Dias [aut, fnd] (Project manager),

Description: Provides set of functions aimed at epidemiologists. They include commands for measures of association and impact for case control studies and cohort studies. They may be particularly useful for outbreak investigations and include univariate and stratified analyses. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands.

Description: Provides set of functions aimed at epidemiologists.
They include commands for measures of association and impact for case control studies and cohort studies.
They may be particularly useful for outbreak investigations and include univariate and stratified analyses.
The functions for cohort studies include the CS(), CSTable() and CSInter() commands.
The functions for case control studies include the CC(), CCTable() and CCInter() commands.