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.

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>.

A system for creating the bilateral trade flow between a country
    pair equal to zero. You provide the data, tell get_zerotradeflow() which 
    variables are of interest and it expands the base by creating the bilateral
    zero trade flow. The bases on the flow of trade between countries only report
    positive trade (greater than zero), however, for some analyzes of gravitacional
    models, data on zero flow is also necessary. Some examples for Gravity Model:
    Figueiredo and Loures (2016) <doi:10.5935/0034-7140.20160015> and
    Yotov, Piermartini, Monteiro and Larch <https://vi.unctad.org/tpa/web/docs/vol2/book.pdf>.

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.