Package: fUtilities
Version: 260.72
Date: 1997 - 2007
Title: Utilities - Rmetrics Function Utilities
Author: Diethelm Wuertz and many others. See the SOURCE file
Depends: R (>= 2.4.0), RUnit, methods, MASS, spatial, tcltk, zoo
Maintainer: Diethelm Wuertz and Rmetrics Core Team <Rmetrics-core@r-project.org>
Description: Environment for teaching "Financial Engineering and Computational Finance"
NOTE: SEVERAL PARTS ARE STILL PRELIMINARY AND MAY BE CHANGED IN THE FUTURE.
THIS TYPICALLY INCLUDES FUNCTION AND ARGUMENT NAMES,
AS WELL AS DEFAULTS FOR ARGUMENTS AND RETURN VALUES.
LazyLoad: yes
LazyData: yes
License: GPL Version 2 or later
URL: http://www.rmetrics.org
Packaged: Mon Oct  8 14:07:08 2007; myself

Title: Rmetrics - Rmetrics Function Utilities
Packaged: Tue May 13 13:43:34 2008; yankee

Packaged: Thu Oct 23 10:16:06 2008; yankee

Packaged: Mon Oct 27 15:25:00 2008; yankee

Description: Frequentist assisted by Bayes (FAB) confidence interval
construction. See 'Adaptive multigroup confidence intervals with constant
coverage' by Yu and Hoff <https://arxiv.org/abs/1612.08287>.

coverage' by Yu and Hoff <DOI:10.1093/biomet/asy009> and
'Exact adaptive confidence intervals for linear regression coefficients'

Title: Overfitting Bayesian Mixtures of Factor Analyzers with
Parsimonious Covariance and Unknown Number of Components
Description: Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <https://www.jstor.org/stable/25703571>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>). Missing values are currently allowed for two model parameterizations.
Imports: Rcpp (>= 0.12.17), MASS, doParallel, foreach, label.switching,
mvtnorm, doRNG, RColorBrewer, corrplot, mclust, coda
Packaged: 2018-10-25 15:55:42 UTC; panagiotis
Author: Panagiotis Papastamoulis [aut, cre]