Collection of tools for regression and classification tasks. The package implements a consistent user interface to the most popular regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, and complements it with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.

Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453> to 
    estimate 50 percent effective dose (ED50) in the study of drug sensitivity. The method has 
    its own advantages for that it can provide robust ED50 estimation. This package contains 
    the modified function of Choi's turning point method.

Methods for color labeling, calculation of eigengenes, merging of closely related modules.

Provides modules as an organizational unit for source code. Modules
    enforce to be more rigorous when defining dependencies and have
    a local search path. They can be used as a sub unit within packages
    or in scripts.

Modular implementation of Multiobjective Evolutionary Algorithms 
              based on Decomposition (MOEA/D) [Zhang and Li (2007), 
              <DOI:10.1109/TEVC.2007.892759>] for quick assembling and 
              testing of new algorithmic components, as well as easy 
              replication of published MOEA/D proposals. The full framework is
              documented in a paper published in the Journal of Statistical 
              Software [<doi:10.18637/jss.v092.i06>].

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.