Computationally efficient tools for comparing all pairs of profiles
    in a DNA database. The expectation and covariance of the summary statistic
    is implemented for fast computing. Routines for estimating proportions of
    close related individuals are available. The use of wildcards (also called F-
    designation) is implemented. Dedicated functions ease plotting the results. 
    See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. 
    Compute the distribution of the numbers of alleles in DNA mixtures. 
    See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.

The goal of 'dndR' is to provide a suite of Dungeons & Dragons related functions.
    This package is meant to be useful both to players and Dungeon Masters (DMs).
    Some functions apply to many tabletop role-playing games (e.g., dice rolling), but others are focused on Fifth Edition (a.k.a. "5e") and where possible both the 2014 and 2024 versions are supported.

Provides a modified hierarchical test (Liu (2017) <doi:10.1214/17-AOS1539>) for detecting the structural difference between two Semiparametric Gaussian graphical models. The multiple testing procedure asymptotically controls the false discovery rate (FDR) at a user-specified level. To construct the test statistic, a truncated estimator is used to approximate the transformation functions and two R functions including lassoGGM() and lassoNPN() are provided to compute the lasso estimates of the regression coefficients.

Provides density, distribution function, quantile function and random
             generation for the split normal and split-t distributions, and computes their
             mean, variance, skewness and kurtosis for the two distributions (Li, F,
             Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).

Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.