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.

Provides methods for Neutrosophic Analysis of Variance
    (NANOVA) for crossover designs and multi-session designs with
    direct and residual effects using interval-valued observations.
    The package computes neutrosophic sums of squares, mean squares,
    interval-valued F-statistics, significance tests, and multiple
    comparisons using Least Significant Difference (LSD) procedures.
    For crisp data, users may enter identical lower and upper response
    values to obtain classical Analysis of Variance (ANOVA) results.
    The basic idea of neutrosophic statistics is obtained from
    Smarandache (2014) <https://fs.unm.edu/NeutrosophicStatistics.pdf>, 
    while the analysis procedures implemented in this package are newly developed.

Provides neutrosophic analysis of variance (NANOVA) and
    analysis of covariance (NANCOVA) for Completely Randomized Designs
    (CRD) and Randomized Complete Block Designs (RCBD) using
    interval-valued observations. Computes interval sums of squares,
    mean squares, F-statistics, significance tests, and interval-based
    least significant difference (LSD) comparisons. When lower and
    upper observations are identical (crisp data), the methods reduce to the
    corresponding classical ANOVA and ANCOVA.

Provides methods for neutrosophic analysis of variance
    (NANOVA) and neutrosophic analysis of covariance (NANCOVA)
    for interval-valued data arising from incomplete block design
    experiments. The package supports balanced incomplete block
    designs (BIBDs), partially balanced incomplete block designs
    (PBIBDs), and lattice designs. Functions are included for
    treatment comparisons, least significant difference (LSD)
    tests, and interval-based statistical inference under
    neutrosophic environments.