Provide data generation and estimation tools for the multivariate scale mixtures of normal 
             presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale 
             mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) 
             <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal 
             presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y>
             and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020)
	     <doi:10.1080/03610918.2020.1804582>.

Estimate necessary sample sizes for comparing the location
    of data from two groups or categories when the distribution of the 
    data is skewed. The package 
    offers a non-parametric method for a Wilcoxon Mann-Whitney test of 
    location shift as well as methods for several generalized linear 
    models, for instance, Gamma regression.

Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.

Provide estimation, simulation, and model selection tools for the skew-unit family
		discussed based on Mukhopadhyay and Brani (1995) <doi:10.2307/2348710>. 
		The family contains extensions for popular distributions such as the ArcSin 
		discussed in Arnold and Groeneveld  (1980) <doi:10.1080/01621459.1980.10477449>, 
		triangular, U-quadratic and Johnson-SB proposed in Cortina-Borja (2006) <doi:10.1111/j.1467-985X.2006.00446_12.x> 
		distributions, among others.

Sequential Kalman filter for scalable online changepoint detection by temporally correlated data. It enables fast single and multiple change points with missing values. See the reference: Hanmo Li, Yuedong Wang, Mengyang Gu (2023), <arXiv:2310.18611>.

SKIFTI files contain brain imaging data in coordinates across Tract Based Spatial Statistics (TBSS) skeleton, which 
    represent the brain white matter intensity values. 
    'skiftiTools' provides a unified environment for reading, writing, 
    visualizing and manipulating SKIFTI-format data. It supports the "subsetting", 
    "concatenating", and using data as data.frame for R statistical functions. The SKIFTI data is structured for convenient access to the data and metadata,
    and includes support for visualizations. For more information see Merisaari et al. (2024) <doi:10.57736/87d2-0608>.