Provides a collection of datasets related to neutrosophic sets for statistical modeling and analysis.

Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.

Nonparametric test of independence between a pair of spatial objects
    (random fields, point processes) based on random shifts with torus or variance correction. See
    Mrkvička et al. (2021) <doi:10.1016/j.spasta.2020.100430>,
    Dvořák et al. (2022) <doi:10.1111/insr.12503>,
    Dvořák and Mrkvička (2024) <doi:10.1080/10618600.2024.2357626>.

Tools for 4D nucleome imaging. 
    Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. 
    See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>.

Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The executable of the model can downloaded from <https://github.com/jeroenpullens/NUCOMBog>.

A comprehensive toolkit for calculating and visualizing Nitrogen Use Efficiency (NUE) indicators in agricultural research. The package implements 23 parameters categorized into fertilizer-based, plant-based, soil-based, isotope-based, ecology-based, and system-based indicators based on Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Key features include vectorized calculations for paired-plot experimental designs, batch processing capabilities for handling large datasets, and built-in visualization tools using 'ggplot2'. Designed to streamline the workflow from raw agronomic data to publication-ready metrics and plots.

A framework for systematic exploration of
    association rules  (Agrawal et al., 1994, <https://www.vldb.org/conf/1994/P487.PDF>), 
    contrast patterns  (Chen, 2022, <doi:10.48550/arXiv.2209.13556>),
    emerging patterns  (Dong et al., 1999, <doi:10.1145/312129.312191>),
    subgroup  discovery (Atzmueller, 2015, <doi:10.1002/widm.1144>),
    and conditional correlations (Hájek, 1978,  <doi:10.1007/978-3-642-66943-9>).
    User-defined functions may also be supplied to guide custom pattern searches.
    Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions
    expressed as elementary conjunctions, evaluates them on a dataset, and
    inspects the induced sub-data for statistical, logical, or structural
    properties such as  associations, correlations, or contrasts. Includes methods 
    for visualization of logical structures and supports interactive exploration
    through integrated Shiny applications.