This is a set of functions to retrieve information about GIMMS
    NDVI3g files currently available online; download (and re-arrange, in the 
    case of NDVI3g.v0) the half-monthly data sets; import downloaded files from 
    ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based 
    on the widespread 'raster' package; conduct quality control; and generate 
    monthly composites (e.g., maximum values) from the half-monthly input data. 
    As a special gimmick, a method is included to conveniently apply the 
    Mann-Kendall trend test upon 'Raster*' images, optionally featuring 
    trend-free pre-whitening to account for lag-1 autocorrelation.

Performs image segmentation in fruit or seeds pictures in order to measure physical features in a high-throughput manner for genome-wide association (GWAS) and genomic selection programs.

Providing various equations to calculate Gini coefficients.
    The methods used in this package can be referenced from Brown MC (1994) <doi: 10.1016/0277-9536(94)90189-9>.

Estimation of the effect of each income source on income inequalities based on the decomposition of Lerman and Yitzhaki (1985) <doi:10.2307/1928447>.

An implementation of a new Gini covariance and correlation to measure dependence between a categorical and numerical variables. Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) <arXiv:1809.09793>.

Estimates the Gini index and computes variances and confidence intervals for finite and infinite populations, using different methods; also computes Gini index for continuous probability distributions, draws samples from continuous probability distributions with Gini indices set by the user; uses 'Rcpp'.
    References: 
    Muñoz et al. (2023) <doi:10.1177/00491241231176847>.
    Álvarez et al. (2021) <doi:10.3390/math9243252>.
    Giorgi and Gigliarano (2017) <doi:10.1111/joes.12185>.
    Langel and Tillé (2013) <doi:10.1111/j.1467-985X.2012.01048.x>.

Density function and generation of random variables from the Generalized Inverse Normal (GIN) distribution from Robert (1991) <doi:10.1016/0167-7152(91)90174-P>. Also provides density functions and generation from the GIN distribution truncated to positive or negative reals. Theoretical guarantees supporting the sampling algorithms and an application to Bayesian estimation of network formation models can be found in the working paper Ding, Estrada and Montoya-Blandón (2023) <https://www.smontoyablandon.com/publication/networks/network_externalities.pdf>.