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.

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>.

Perform the calculation of W-test, diagnostic checking, calculate
    minor allele frequency (MAF) and odds ratio.

Evaluation of alternatives based on multiple criteria using weighted technique for Order preference by similarity to an ideal solution method. Reference: Hwang CL. (1981, ISBN:978-3-540-10558-9).

Allows form managers to download entries from their respondents
    using Wufoo JSON API (<https://www.wufoo.com>). Additionally, the Wufoo reports - when public - can be
    also acquired programmatically. Note that building new forms within this package
    is not supported.

Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.

Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'.  
   Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, 
   conditioned scatter plot with marginal densities,
   box and stem with matching theoretical distribution, and density with matching theoretical distribution.