Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.

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).
    All functions currently focus on Fifth Edition (a.k.a. "5e") but once the next edition is published functions will likely be expanded to include any rule changes.

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>).