Provides an interface to 'vinecopulib', a C++ library for vine 
 copula modeling. The 'rvinecopulib' package implements the core features of the
 popular 'VineCopula' package, in particular inference algorithms for both vine 
 copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker 
 and more modern API, improved performances, especially in high dimensions, 
 nonparametric and multi-parameter families, and the ability to model discrete 
 variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only 
 C++ library (currently version 0.7.2). Thus users do not need to install 
 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial 
 releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' 
 is licensed under the GNU GPL version 3.

The Regional Vulnerability Index (RVI), a statistical measure of brain structural abnormality, quantifies an individual's similarity to the expected pattern (effect size) of deficits in schizophrenia (Kochunov P, Fan F, Ryan MC, et al. (2020) <doi:10.1002/hbm.25045>).

Open any data frame with 'visidata', a terminal-based spreadsheet application <https://www.visidata.org>.

Load data from vk.com api about your communiti users and views,
    ads performance, post on user wall and etc.	For more information 
    see API Documentation <https://vk.com/dev/first_guide>.

Has various functions designed to implement the 
    Hermite-Gaussian Radial Velocity (HGRV) estimation approach of 
    Holzer et al. (2020) <arXiv:2005.14083>, which is a particular application of the radial 
    velocity method for detecting exoplanets. The overall approach consists 
    of four sequential steps, each of which has a function in this package: 
    (1) estimate the template spectrum with the function estimate_template(), 
    (2) find absorption features in the estimated template with the function 
    findabsorptionfeatures(), (3) fit Gaussians to the absorption features 
    with the function Gaussfit(), (4) apply the HGRV with simple linear 
    regression by calling the function hgrv(). This package is meant to be 
    open source. But please cite the paper Holzer et al. (2020) <arXiv:2005.14083> when 
    publishing results that use this package.