Perform analysis of variance when the experimental units are spatially correlated. There are two methods to deal with spatial dependence: Spatial autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019) <doi:10.28951/rbb.v37i2.388>) and geostatistics (see Pontes, J. M., & Oliveira, M. S. D. (2004) <doi:10.1590/S1413-70542004000100018>). For both methods, there are three multicomparison procedure available: Tukey, multivariate T, and Scott-Knott.

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.