Provides functions that automate accessing, downloading and exploring Soil Moisture
    and Ocean Salinity (SMOS) Level 4 (L4) data developed by Barcelona Expert Center (BEC). 
    Particularly, it includes functions to search for, acquire, extract, and plot BEC-SMOS L4 soil 
    moisture data downscaled to ~1 km spatial resolution. Note that SMOS is one of Earth Explorer 
    Opportunity missions by the European Space Agency (ESA). More information about SMOS  
    products can be found at <https://earth.esa.int/eogateway/missions/smos/data>.

A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor.  Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.

Provides the SMOTE with Boosting (SMOTEWB) algorithm. See
      F. Sağlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a
      SMOTE-based resampling technique which creates synthetic data on the links 
      between nearest neighbors. SMOTEWB uses boosting weights to determine where
      to generate new samples and automatically decides the number of neighbors 
      for each sample. It is robust to noise and outperforms most of the 
      alternatives according to Matthew Correlation Coefficient metric.
      Alternative resampling methods are also available in the package.

Provides movies to help students to understand statistical 
  concepts.  The 'rpanel' package  <https://cran.r-project.org/package=rpanel> 
  is used to create interactive plots that move to illustrate key statistical 
  ideas and methods.  There are movies to: visualise probability distributions
  (including user-supplied ones); illustrate sampling distributions of the
  sample mean (central limit theorem), the median, the sample maximum 
  (extremal types theorem) and (the Fisher transformation of the) 
  product moment correlation coefficient; examine the influence of an 
  individual observation in simple linear regression; illustrate key concepts 
  in statistical hypothesis testing. Also provided are dpqr functions for the 
  distribution of the Fisher transformation of the correlation coefficient 
  under sampling from a bivariate normal distribution.