Latent Residuals Test for Epidemiology and Ecology Models
Latent residual tests recently developed (Lau et al. 2014) which
are specifically designed to measure the goodness-of-fit of different model
components of a general spatial S-E-I-R model commonly used in epidemiology
and ecology studies. More restrictive model classes such as S-I-R can be
accommodated. Note that although each test is specifically designed to be
sensitive to one particular component of a spatio-temporal model, they are
not theoretically restricted to only testing these specific aspects and can
be used as a general model assessment tool like any other conventional
model selection techniques. The underlying functions are coded in C++ so
they should be generally quick.
Under the null hypothesis that the model is correct, the
residuals follow distribution U(0,1). The examples only compute
a single set of residuals given actual values of most of the
model components which are typically required to be imputed in
data augmentation schemes such as MCMC in which multiple sets
of residuals can be imputed. Only the raw residuals are
outputted, a natural summary statistics of the residual samples
is the p-value which may be computed using Kolmogorov-Smirnov
or Anderson-Darling test.

Ensemble Based Machine Learning Approach for Predicting
Methylation States
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"))
Dipro Sinha [aut, cre],
Sunil Archak [aut],
Dwijesh Chandra Mishra [aut],
Tanwy Dasmandal [aut],
Md Yeasin [aut]
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>.
stats, devtools, tidyverse, seqinr, Biostrings,
splitstackshape, entropy, party, stringr, tibble, doParallel,
parallel, e1071, caret, randomForest, gbm, foreach, ftrCOOL,
iterators