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.

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.

It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.

This is a Google Forms and Google Classroom API Wrapper for R for managing Google Classrooms from R. The documentation for these APIs is here <https://developers.google.com/forms/api/guides> .