The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. 
             The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Provides methods for determining optimum plot size and shape in field 
    experiments using Fairfield-Smith's variance law approach. It will evaluate 
    field variability, determine optimum plot size and shape and study fertility 
    trends across the field.

Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.

Use optimization to estimate weights that balance covariates for binary, multi-category, continuous, and multivariate treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates, as in matching-adjusted indirect comparison (MAIC).

Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data.  The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift.  See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.

Reads data from Bruker OPUS binary files of Fourier-Transform
    infrared spectrometers of the company Bruker Optics GmbH & Co.
    This package is released independently from Bruker, and Bruker and OPUS are
    registered trademarks of Bruker Optics GmbH & Co. KG.
    <https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/latest-release.html>.
    It lets you import both measurement data and parameters from OPUS files.
    The main method is `read_opus()`, which reads one or multiple OPUS files
    into a standardized list class. Behind the scenes, the reader parses the
    file header for assigning spectral blocks and reading binary data from the
    respective byte positions, using a reverse engineering approach. Infrared
    spectroscopy combined with chemometrics and machine learning is an
    established method to scale up chemical diagnostics in various industries
    and scientific fields.