Implements fast Monte Carlo simulations for 
    goodness-of-fit (GOF) tests for discrete distributions. This 
    includes tests based on the Chi-squared statistic, the 
    log-likelihood-ratio (G^2) statistic, the Freeman-Tukey 
    (Hellinger-distance) statistic, the Kolmogorov-Smirnov 
    statistic, the Cramer-von Mises statistic as described in 
    Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>, 
    and the root-mean-square statistic, see Perkins, 
    Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.

Implementations of several multiple testing procedures that control
    the family-wise error rate (FWER) designed specifically for discrete tests.
    Included are discrete adaptations of the Bonferroni, Holm, Hochberg and
    Šidák procedures as described in the papers Döhler (2010) "Validation of
    credit default probabilities using multiple-testing procedures"
    <doi:10.21314/JRMV.2010.062> and Zhu & Guo (2019) "Family-Wise Error Rate
    Controlling Procedures for Discrete Data" 
    <doi:10.1080/19466315.2019.1654912>. The main procedures of this package
    take as input the results of a test procedure from package 'DiscreteTests'
    or a set of observed p-values and their discrete support under their nulls.
    A shortcut function to apply discrete procedures directly to data is also
    provided.

Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.

Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.

We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), <doi:10.1073/pnas.1530509100>. The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values.