Title: Extensible Classes and Methods for Penalized-Regression-based
Description: Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.

Title: Extensible Classes and Methods for Penalized-Regression-Based

Title: Tools for the estimation of entropy and related quantities.
Author: Lijuan Cao <lcao@jcsu.edu> and Michael Grabchak <mgrabcha@uncc.edu>
Description: This package contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, and Kullback-Leibler divergence. The estimators used have a bias that decays exponentially fast.

Title: Estimation of Entropy and Related Quantities
Description: Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.

Title: Tools for Exploring Differential Shannon Entropy, Differential
Coefficient of Variation and Differential Expression
Author: Kai Wang, Charles A. Phillips, Arnold M. Saxton and Michael A. Langston
Description: Rows of two matrices are compared for Shannon entropy,
coefficient of variation, and expression. P-values can be requested for all metrics.

Title: MCMC Simulation and Convergence Evaluation using Entropy and
Authors@R: c(person("Didier", "Chauveau", role = c("aut", "cre"),
person("Houssam", "Alrachid", role = "ctb"))
Description: Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) <doi:10.1051/ps/2012004> and next articles.