A flexible statistical framework for network-valued data analysis. 
    It leverages the complexity of the space of distributions on graphs by using 
    the permutation framework for inference as implemented in the 'flipr' package. 
    Currently, only the two-sample testing problem is covered and generalization 
    to k samples and regression will be added in the future as well. It is a 
    4-step procedure where the user chooses a suitable representation of the 
    networks, a suitable metric to embed the representation into a metric space, 
    one or more test statistics to target specific aspects of the distributions 
    to be compared and a formula to compute the permutation p-value. Two types 
    of inference are provided: a global test answering whether there is a 
    difference between the distributions that generated the two samples and a 
    local test for localizing differences on the network structure. The latter 
    is assumed to be shared by all networks of both samples. References: Lovato, 
    I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for 
    network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A., 
    Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis 
    testing for network-valued data: Analysis of brain networks of patients with 
    autism" <doi:10.1111/rssc.12463>.

In the working paper titled "Why You Should Never Use the Hodrick-Prescott 
   Filter", James D. Hamilton proposes a new alternative to economic time series 
   filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.

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.