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.

Methods for maximum likelihood and Bayesian estimation for the Wishart mixture model and the mixture-of-experts Wishart (MoE-Wishart) model. The package provides four inference algorithms for these models, each implemented using the expectation–maximization (EM) algorithm for maximum likelihood estimation and a fully Bayesian approach via Gibbs-within-Metropolis–Hastings sampling.

Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Xiao, Joseph, and Ray (2022) <doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT design can be viewed as an improvement to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991) <doi:10.1080/00401706.1991.10484804>. The improvement is achieved by exploiting the connection between Morris screening designs and Monte Carlo-based Sobol' designs, and optimizing the design using a space-filling criterion. This work is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.