Within-subject mediation analysis using structural equation modeling.
    Examine how changes in an outcome variable between two conditions are mediated 
    through one or more variables. Supports within-subject mediation analysis using 
    the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, 
    and extends Monte Carlo confidence interval estimation to missing data scenarios 
    using the 'semmcci' package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.

A weighted selection probability to locate rare variants associated with multiple phenotypes.

A parallel implementation of Weighted Subspace Random Forest.  The
    Weighted Subspace Random Forest algorithm was proposed in the
    International Journal of Data Warehousing and Mining by Baoxun Xu,
    Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye
    (2012) <DOI:10.4018/jdwm.2012040103>.  The algorithm can classify
    very high-dimensional data with random forests built using small
    subspaces.  A novel variable weighting method is used for variable
    subspace selection in place of the traditional random variable
    sampling.This new approach is particularly useful in building
    models from high-dimensional data.

Implements the Welch-Satterthwaite approximation for differences
    of non-standardized t-distributed random variables in both univariate and 
    multivariate settings. The package provides methods for computing effective
    degrees of freedom and scale parameters, as well as distribution functions
    for the approximated difference distribution. The methodology extends the
    classical Welch-Satterthwaite framework from variance combinations to
    t-distribution differences through careful moment matching. Methods build
    on the classical Welch-Satterthwaite approach described in Welch (1947)
    <doi:10.1093/biomet/34.1-2.28> and Satterthwaite (1946) <doi:10.2307/3002019>.

Tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.