Tools for reading, visualising and processing Magnetic Resonance
    Spectroscopy data. The package includes methods for spectral fitting: Wilson
    (2021) <DOI:10.1002/mrm.28385>, Wilson (2025) <DOI:10.1002/mrm.30462> and
    spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Provides a comprehensive suite of portfolio spanning tests for asset 
             pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, 
             Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, 
             Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and 
             Luger (2016) <doi:10.1080/07350015.2015.1019510>.

Implements the sparse clustering methods of Witten and
        Tibshirani (2010): "A framework for feature selection in
        clustering"; published in Journal of the American Statistical
        Association 105(490): 713-726.

A flexible framework combining 
  variable screening and random projection techniques for fitting ensembles of 
  predictive generalized linear models to high-dimensional data.
  Designed for extensibility, the package implements
  key techniques as S3 classes with user-friendly constructors,
  enabling easy integration and development of new procedures for 
  high-dimensional applications. For more details see 
  Parzer et al (2024a) <doi:10.48550/arXiv.2312.00130> and
  Parzer et al (2024b) <doi:10.48550/arXiv.2410.00971>.

Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the 'spark-sas7bdat' 'Spark' package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data.

Load Avro Files into 'Apache Spark' using 'sparklyr'. This
    allows to read files from 'Apache Avro' <https://avro.apache.org/>.