Title: Variance Estimation using Difference-Based Methods
Author: Wenlin Dai <wenlin.dai@kaust.edu.sa>, Tiejun Tong <tongt@hkbu.edu.sa>.
Description: Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.

Title: Variation of effective population size
Author: Natacha Nikolic and Claude Chevalet
Description: Functions for estimate the changes of effective population

Title: A Framework for Variance Reduction
Description: In order to make it easy to use variance reduction algorithms for any simulation, this framework can help you.
We propose user friendly and easy to extend framework. Antithetic Variates, Inner Control Variates,
Outer Control Variates and Importance Sampling algorithms are available in the framework. User can
write its own simulation function and use the Variance Reduction techniques in this package to
obtain more efficient simulations. An implementation of Asian Option simulation is already
available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) <doi:10.1016/j.ejor.2012.03.046>.
Packaged: 2020-12-06 20:26:04 UTC; onurboyar

Authors@R: c(person("Kristy","Robledo", email = "robledo.kristy@gmail.com", role = c("aut", "cre")),person("Ian", "Marschner", role = c("ths")))
Description: Methods for fitting semi-parametric mean and variance models, with normal or censored data. Also extended to allow a regression in the location, scale and shape parameters.
Imports: splines, stats, graphics, sn, survival, utils

Title: Variable Selection for Model-Based Clustering using the
Integrated Complete-Data Likelihood of a Latent Class Model
Description: Variable Selection for model-based clustering by using a mixture model of Gaussian distributions
assuming conditional independence between variables. The algorithm carries out the model
selection by optimizing the MICL criterion which has a closed form for such a distribution.
Imports: methods, Rcpp (>= 0.11.1), parallel, mclust

Description: Uses a finite mixture model for performing the cluster analysis with variable selection of continuous data by assuming independence between classes. The package deals dataset with missing values by assuming that values are missing at random. The one-dimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by the Maximum Integrated Complete-Data Likelihood criterion. The maximum likelihood inference is done by an EM algorithm for the selected model. This package also performs the imputation of  missing values.
Collate: 'CheckInputs.R' 'DataCstr.R' 'VSLCMGrlClasses.R' 'ICLexact.R'