Bayesian adaptive randomization is also called outcome adaptive randomization, which is increasingly used in clinical trials.

Tool to find where a function has its lowest value(minimum). The
    functions can be any dimensions. Recommended use is with eps=10^-10, but can be
    run with 10^-20, although this depends on the function. Two more methods are in
    this package, simple gradient method (Gradmod) and Powell method (Powell). These
    are not recommended for use, their purpose are purely for comparison.

The function \code{barcode()}
        produces a histogram-like plot of a distribution that shows
        granularity in the data.

Tools to generate unique identifier codes and
    printable barcoded labels for the management of biological samples.
    The creation of unique ID codes and printable PDF files can be
    initiated by standard commands, user prompts, or through a GUI addin
    for R Studio. Biologically informative codes can be included for
    hierarchically structured sampling designs.

To perform species identification using DNA barcodes.

Simultaneously clusters the Periodontal diseases (PD) patients 
    and their tooth sites after taking the patient- and site-level covariates into consideration. 
    'BAREB' uses the determinantal point process (DPP) prior to induce diversity 
    among different biclusters to facilitate parsimony and interpretability. 
    Essentially, 'BAREB' is a cluster-wise linear model based on Yuliang (2020) <doi:10.1002/sim.8536>.

Bayesian Additive Regression Kernels (BARK) provides
	an implementation for non-parametric function estimation using Levy
	Random Field priors for functions that may be represented as a
	sum of additive multivariate kernels.  Kernels are located at
	every data point as in Support Vector Machines, however, coefficients 
	may be heavily shrunk to zero under the Cauchy process prior, or even, 
	set to zero.  The number of active features is controlled by priors on
	precision parameters within the kernels, permitting feature selection. For 
	more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels",
	Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, 
	and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy
	Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 
	<doi:10.1214/11-AOS889>.