Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.

Easily override the default visual choices in 'ggplot2' to make 
    your time series plots look more like the Wall Street Journal. Specific 
    theme design choices include omitting x-axis grid lines and displaying 
    sparse light grey y-axis grid lines. Additionally, this allows to label 
    the y-axis scales with your units only displayed on the top-most number, 
    while also removing the bottom most number (unless specifically 
    overridden). The goal is visual simplicity, because who has time to waste 
    looking at a cluttered graph?

Entropy weighted k-means (ewkm) by Liping Jing, Michael
        K. Ng and Joshua Zhexue Huang (2007)
        <doi:10.1109/TKDE.2007.1048> is a weighted subspace clustering
        algorithm that is well suited to very high dimensional data.
        Weights are calculated as the importance of a variable with
        regard to cluster membership.  The two-level variable
        weighting clustering algorithm tw-k-means (twkm) by Xiaojun
        Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013)
        <doi:10.1109/TKDE.2011.262> introduces two types of weights,
        the weights on individual variables and the weights on
        variable groups, and they are calculated during the clustering
        process.  The feature group weighted k-means (fgkm) by Xiaojun
        Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012)
        <doi:10.1016/j.patcog.2011.06.004> extends this concept by
        grouping features and weighting the group in addition to
        weighting individual features.

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