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>.

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

A 'sparklyr' extension package providing an integration with Google 'BigQuery'.
  It supports direct import/export where records are directly streamed from/to 'BigQuery'.
  In addition, data may be imported/exported via intermediate data extracts on Google 'Cloud Storage'.

'Hail' is an open-source, general-purpose, 'python' based data analysis
    tool with additional data types and methods for working with genomic data, see
    <https://hail.is/>. 'Hail' is built to scale and has first-class support for 
    multi-dimensional structured data, like the genomic data in a genome-wide 
    association study (GWAS). 'Hail' is exposed as a 'python' library, using primitives
    for distributed queries and linear algebra implemented in 'scala', 'spark', and 
    increasingly 'C++'. The 'sparkhail' is an R extension using 'sparklyr' package. 
    The idea is to help R users to use 'hail' functionalities with the well-know
    'tidyverse' syntax, see <https://www.tidyverse.org/>.