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.

Advanced forecasting algorithms for long-term energy demand at the 
  national or regional level. The methodology is based on Grandón et al. (2024) 
  <doi:10.1016/j.apenergy.2023.122249>; Zimmermann & Ziel (2024) 
  <doi:10.1016/j.apenergy.2025.125444>. Real-time data, including power demand, weather conditions, and macroeconomic indicators, are provided through automated API integration with various institutions. The modular approach maintains transparency on the various model selection processes and encompasses the ability to be adapted to individual needs. 'oRaklE' tries to help facilitating robust decision-making in energy management and planning.