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.

Hydrologic modelling system is an object oriented tool for simulation and analysis of hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of a hydrologic model.

Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>.

A useful statistical tool for the construction and analysis of Honeycomb Selection Designs. More information about this type of designs: Fasoula V. (2013) <doi:10.1002/9781118497869.ch6> Fasoula V.A., and Tokatlidis I.S. (2012) <doi:10.1007/s13593-011-0034-0> Fasoulas A.C., and Fasoula V.A. (1995) <doi:10.1002/9780470650059.ch3> Tokatlidis I. (2016) <doi:10.1017/S0014479715000150> Tokatlidis I., and Vlachostergios D. (2016)  <doi:10.3390/d8040029>.