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

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>.
dplyr (>= 1.0.0), tidyr (>= 1.0.0), jsonlite, httr, stringr,
lgr

Performs binary classification of data using a Relevance
Vector Machine

Performs binary classification and regression of data
using a Relevance Vector Machine
Previous address reported this as current in Oct 2012.

Radial Velocity Method for Detecting Exoplanets
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.

When a gene or a genetic region is significantly associated with a disease or a trait, the rare variant selection procedure is able to distinguish causal (risk or protective) rare variants from noncausal rare variants located within the same gene or the same genetic region.

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.