particle learning (PL).  The sequential nature of inference
is provides for optimization in the presence of unknown constraints.

Package: plgp
Type: Package
Title: Particle Learning of Gaussian Processes
Version: 1.1
Date: 2010-09-20
Author: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Maintainer: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
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. See the examples section of ?plgp and
demo(package="plgp") for an index of demos
Depends: R (>= 2.4), mvtnorm, tgp
Suggests: akima, ellipse, splancs
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/plgp.html
Packaged: 2010-09-20 18:12:03 UTC; rgramacy
Repository: CRAN
Date/Publication: 2010-09-21 08:45:38

Author: Robert Tibshirani, Jerome Friedman
Description: Fits a pliable lasso model.  For details see Tibshirani and Friedman (2018) <arXiv:1712.00484>.
Packaged: 2019-02-11 21:25:32 UTC; robtibshirani

Description: Provides tools for image manipulation that will help you to
quantify plant leaf area, disease severity, number of disease lesions,
and obtain statistics of image objects such as grains, pods, pollen,
leaves, and more. Tools for segment images and create binary images
using the method of automatic threshold selection method proposed by
Author: Tiago Olivoto [aut, cre] (<https://orcid.org/0000-0002-0241-9636>)

leaves, and more. Tools to segment images and create binary images
using the method of automatic threshold selection proposed by