Provides functions for estimating sparse precision matrices
    using a random graphical model framework under an L0-style penalty.
    The method evaluates candidate theta values and returns both
    continuous and binary precision matrices representing inferred
    network structures.

Algorithms for estimating robustly the parameters of a Gaussian, Student, or Laplace Mixture Model.

Implementation of the Robust Gauss-Newton (RGN) algorithm,
    designed for solving optimization problems with a sum of least squares
    objective function. For algorithm details please refer to Qin et. al. (2018) 
    <doi:10.1029/2017WR022488>.

Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [<doi:10.1109/ACCESS.2021.3123151>] and Arnaiz-Gonzalez et al. (2016) [<doi:10.1016/j.eswa.2015.12.046>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.

Parse scientific names using 'gnparser'
    (<https://github.com/gnames/gnparser>), written in Go. 'gnparser'
    parses scientific names into their component parts; it utilizes
    a Parsing Expression Grammar specifically for scientific names.

Routines that allow the user to run a large number of goodness-of-fit tests.  
     It allows for data to be continuous or discrete. It includes routines to estimate
     the power of the tests and display them as a power graph.
     The routine run.studies allows a user to quickly study the power of a new method and 
     how it compares to some of the standard ones.

Interface for loading data from 'Google Ads API', 
    see <https://developers.google.com/google-ads/api/docs/start>. 
    Package provide function for authorization and loading reports.