Non-parametric clustering of joint pattern multi-genetic/epigenetic factors. This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) <doi:10.2307/2346830> framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions.

Here we performs robust hierarchical co-clustering between row and column entities of a data matrix in 
             absence and presence of outlying observations. It can be used to explore important co-clusters consisting of  
             important samples and their regulatory significant features. Please see Hasan, Badsha and Mollah (2020) 
             <doi:10.1101/2020.05.13.094946>.

An implementation of the 'Heroicons' icon library for 'shiny'
    applications and other 'R' web-based projects. You can search, render,
    and customize icons without 'CSS' or 'JavaScript' dependencies.

Implements the hierarchical Bayesian analysis of populations structure (hierBAPS) 
  algorithm of Cheng et al. (2013) <doi:10.1093/molbev/mst028> for clustering DNA sequences 
  from multiple sequence alignments in FASTA format. 
  The implementation includes improved defaults and plotting capabilities 
  and unlike the original 'MATLAB' version removes singleton SNPs by default.

A framework that supports creating and extending enterprise Shiny applications using best practices.

Implements simple Hamiltonian Monte Carlo routines in R for
             sampling from any desired target distribution which is continuous
             and smooth. See Neal (2017) <arXiv:1701.02434> for further details
             on Hamiltonian Monte Carlo. Automatic parameter selection is not
             supported.