Single objective optimization using a CMA-ES.

Pure R implementation of the Covariance Matrix Adaptation -
    Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).

It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance and Chebyshev distance between each pair of species in a list of data frames. These metrics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts.

Contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.

Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>. 
  It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.

An implementation of 'Conic Multivariate Adaptive Regression Splines (CMARS)' in R.
    See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with 
    multivariate adaptive regression splines supported by continuous optimization, 
    <DOI:10.1080/17415977.2011.624770>. It constructs models by using the terms
    obtained from the forward step of MARS and then estimates parameters by using 
    'Tikhonov' regularization and conic quadratic optimization. It is possible to 
    construct models for prediction and binary classification. It provides performance 
    measures for the model developed. The package needs the optimisation software 'MOSEK' 
    <https://www.mosek.com/> to construct the models. Please follow the instructions in 
    'Rmosek' for the installation.

Conditional mixture model fitted via EM (Expectation Maximization) algorithm for model-based clustering, including parsimonious procedure, optimal conditional order exploration, and visualization.