The aim is to develop an R package, which is the 'new.dist'
    package, for the probability (density) function, the
    distribution function, the quantile function and the
    associated random number generation function for discrete and
    continuous distributions, which have recently been proposed
    in the literature. This package implements the following
    distributions: The Power Muth Distribution, a Bimodal Weibull
    Distribution, the Discrete Lindley Distribution, The
    Gamma-Lomax Distribution, Weighted Geometric Distribution, a
    Power Log-Dagum Distribution, Kumaraswamy Distribution,
    Lindley Distribution, the Unit-Inverse Gaussian Distribution,
    EP Distribution, Akash Distribution, Ishita Distribution,
    Maxwell Distribution, the Standard Omega Distribution,
    Slashed Generalized Rayleigh Distribution, Two-Parameter
    Rayleigh Distribution, Muth Distribution, Uniform-Geometric
    Distribution, Discrete Weibull Distribution.

Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.

Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.