Package: earlywarnings
Type: Package
Title: Early Warning Signals Toolbox for Detecting Critical Transitions
in Timeseries
Version: 1.0.59
Date: 2013-04-07
Author: Vasilis Dakos <vasilis.dakos@gmail.com>, with contributions from S.R.
Carpenter, T. Cline, L. Lahti
Maintainer: Vasilis Dakos <vasilis.dakos@gmail.com>
Description: The Early-Warning-Signals Toolbox provides methods for estimating
statistical changes in timeseries that can be used for identifying nearby
critical transitions. Based on Dakos et al (2012) Methods for Detecting
Early Warnings of Critical Transitions in Time Series Illustrated Using
Simulated Ecological Data. PLoS ONE 7(7):e41010
Depends: R (>= 3.0.2), ggplot2, moments, tgp, tseries
Imports: fields, nortest, quadprog, Kendall, KernSmooth, lmtest, som, spam, stats
LazyLoad: yes
URL: http://www.early-warning-signals.org
http://vasilisdakos.wordpress.com
License: FreeBSD
Packaged: 2014-04-12 07:32:14 UTC; vasilisdakos
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-04-12 23:25:14
Package: plgp
Type: Package
Title: Particle Learning of Gaussian Processes
Version: 1.1-7
Date: 2014-11-27
Author: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Description: Sequential Monte Carlo inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL). The sequential nature of inference
and the active learning (AL) hooks provided facilitate thrifty
sequential design (by entropy) and optimization
(by improvement) for classification and
regression models, respectively.
This package essentially provides a generic
PL interface, and functions (arguments to the interface) which
implement the GP models and AL heuristics. Functions for
a special, linked, regression/classification GP model and
an integrated expected conditional improvement (IECI) statistic
is provides for optimization in the presence of unknown constraints.
Separable and isotropic Gaussian, and single-index correlation
functions are supported.
See the examples section of ?plgp and demo(package="plgp")
for an index of demos
Depends: R (>= 2.4), mvtnorm, tgp
Suggests: ellipse, splancs, akima
License: LGPL
URL: http://faculty.chicagobooth.edu/robert.gramacy/plgp.html
Packaged: 2014-11-28 12:19:42 UTC; bobby
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-12-02 00:14:32
providing 
.