Here, a function has been developed to generate parameters of the input designs, as well as incidence matrices. This is a general function that can be used to investigate the characterization properties of any block design.

Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.

A 'shiny' application that enables the user to create a
    prototype UI, being able to drag and drop UI components before being
    able to save or download the equivalent R code.

Intelligently assign samples to batches in order to reduce batch effects.
    Batch effects can have a significant impact on data analysis, especially
    when the assignment of samples to batches coincides with the contrast
    groups being studied. By defining a batch container and a scoring function
    that reflects the contrasts, this package allows users to assign samples in
    a way that minimizes the potential impact of batch effects on the
    comparison of interest. Among other functionality, we provide an
    implementation for OSAT score by Yan et al. (2012,
    <doi:10.1186/1471-2164-13-689>).

A simple interface to build designs using the package 'DeclareDesign'. 
    In one line of code, users can specify the parameters of individual 
    designs and diagnose their properties. The designers can also be used 
    to compare performance of a given design across a range of combinations 
    of parameters, such as effect size, sample size, and assignment probabilities.

Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'highs' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at <https://www.gurobi.com/documentation/quickstart.html> and <https://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>.  We have also included directions in the gurobi_installation file in the inst folder.