Calculate the precision in mean differences (raw or Cohen's D) and 
    correlation coefficients for different sample sizes. Uses permutations of the
    collected functional magnetic resonance imaging (fMRI) region of interest data.
    Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) 
    <doi:10.31234/osf.io/cz32t>.

Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.

Analyzes data involving imprecise and vague information. Provides summary statistics and describes the characteristics of neutrosophic data, as defined by Florentin Smarandache (2013).<ISBN:9781599732749>.

Apply neutrosophic regression type estimator and performs neutrosophic interval analysis including metric calculations for survey data.

Draw nested extreme value random variables, which are the variables that appear in the latent variable formulation of the nested logit model.

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>.