Variable/Feature selection in high or ultra-high dimensional
    settings has gained a lot of attention recently specially in cancer genomic
    studies. This package provides a Bayesian approach to tackle this problem,
    where it exploits mixture of point masses at zero and nonlocal priors to
    improve the performance of variable selection and coefficient estimation.
    product moment (pMOM) and product inverse moment (piMOM) nonlocal priors
    are implemented and can be used for the analyses. This package performs
    variable selection for binary response and survival time response datasets
    which are widely used in biostatistic and bioinformatics community.
    Benefiting from parallel computing ability, it reports necessary outcomes
    of Bayesian variable selection such as Highest Posterior Probability Model
    (HPPM), Median Probability Model (MPM) and posterior inclusion probability
    for each of the covariates in the model. The option to use Bayesian Model
    Averaging (BMA) is also part of this package that can be exploited for
    predictive power measurements in real datasets.

Implements a backward procedure for single and multiple change point detection proposed by Shin et al. <arXiv:1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.

Whole-genome regression methods on Bayesian framework fitted via EM
    or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>),
    with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).

Package for Breed Wheat Genomic Selection Pipeline. 
    The R package 'BWGS' is developed by Louis Gautier Tran <louis.gautier.tran@gmail.com> and Gilles Charmet <gilles.charmet@inra.fr>.
    This repository is forked from original repository <https://forgemia.inra.fr/umr-gdec/bwgs>
    and modified as a R package.

A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.