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>.

A workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework.
    References: 
            Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.