Fetch statistics about views, downloads and data volume from 'Zenodo' deposits. The package collects a 'Zenodo' (<https://zenodo.org>) deposit file information, respecting the website scrapping policies.

Procedures for calculation, plotting, animation, and approximation of the outputs for fuzzy numbers (see A.I. Ban, L. Coroianu, P. Grzegorzewski "Fuzzy Numbers: Approximations, Ranking and Applications" (2015)) based on the Zadeh's Extension Principle (see de Barros, L.C., Bassanezi, R.C., Lodwick, W.A. (2017) <doi:10.1007/978-3-662-53324-6_2>).

Provides a structured framework for consistent user
    communication and configuration management for package developers.

Uses bootstrap to test zero order correlation being equal to a 
    partial or semi-partial correlation (one or two tailed). Confidence 
    intervals for the parameter (zero order minus partial) can also be
    determined. Implements the bias-corrected and accelerated bootstrap
    method as described in "An Introduction to the Bootstrap" Efron (1983) 
    <0-412-04231-2>.

Functions to compute compositional turnover using zeta-diversity,
    the number of species shared by multiple assemblages. The package includes
    functions to compute zeta-diversity for a specific number of
    assemblages and to compute zeta-diversity for a range of numbers of
    assemblages. It also includes functions to explain how zeta-diversity
    varies with distance and with differences in environmental variables
    between assemblages, using generalised linear models, linear models
    with negative constraints, generalised additive models,shape
    constrained additive models, and I-splines.

The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we  have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.