A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.

A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajMinKmeans' are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator.

Create dummy variables from categorical data.
    This package can convert categorical data (factor and ordered) into
    dummy variables and handle multiple columns simultaneously.
    This package enables to select whether a dummy variable for base group
    is included (for principal component analysis/factor analysis) or
    excluded (for regression analysis) by an option.
    'makedummies' function accepts 'data.frame', 'matrix', and
    'tbl' (tibble) class (by 'tibble' package).
    'matrix' class data is automatically converted to 'data.frame' class.

A user-friendly interface for the construction of
        'Makefiles'.

A user-friendly tool for visualizing categorical or group movement.

Automation tool to run R scripts if needed, based on last modified
  time. It comes with no package dependencies, organizational overhead, or
  structural requirements. In short: run an R script if underlying files have
  changed, otherwise do nothing.