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.

Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher distribution on a sphere. This method is fast and efficient when generating a large number of pseudo-random vectors. Functions to generate random variates and compute density for the distribution of an inner product between von Mises-Fisher random vector and its mean direction are also provided. Details are in Kang and Oh (2024) <doi:10.1007/s11222-024-10419-3>.

Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described 
    in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes
    the total variance predicted in a regression model into weights that accurately reflect the proportional 
    contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios,
    RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.