====== Toolboxes and softwares for learning and optimization ======

===== DiceKriging / DiceOptim / DiceView =====
//NKO participant: C. Chevalier, D. Ginsbourger, V. Picheny, Y. Richet//

Kriging-based R packages. DiceKriging provides estimation, validation and prediction of kriging models; DiceOptim implements the EGO algorithm and its variants; DiceView allows quick vizualization of kriging models (with 2D/3D section views for high dimensions).

All the packages provide a full documentation with simple examples. 

The package DiceOptim does not provide yet algorithms for noisy optimization, but a new version should be released soon. alpha-version of noisy kriging-based optimizers are available on demand (//victor "dot" picheny "at" ecp "dot" fr// or //david "dot" ginsbourger "at" stat "dot" unibe "dot" ch//).

The packages are downloadable directly from R or at the following URLs:

    * DiceKriging: http://cran.r-project.org/web/packages/DiceKriging/index.html

    * DiceOptim: http://cran.r-project.org/web/packages/DiceOptim/index.html

    * DiceView: http://cran.r-project.org/web/packages/DiceView/index.html

{{:rudolph:kriging:krigindice.png|}}

===== SURROGATES toolbox =====
//NKO participant: Felipe Viana//

SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and surrogate-based optimization methods. The current version includes the following capabilities:

    * **Design of experiments:** central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs.
    * **Surrogates:** kriging, polynomial response surface, radial basis neural network, and support vector regression.
    * **Analysis of error and cross validation:** leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others).
    * **Surrogate-based optimization:** efficient global optimization (EGO) algorithm.
    * **Other capabilities:** global sensitivity analysis and conservative surrogates via safety margin.

URL: http://sites.google.com/site/fchegury/surrogatestoolbox

===== Engineering Design via Surrogate Modelling: A Practical Guide Matlab code =====
//NKO participant: Alexander Forrester//

A large set of matlab functions that accompany the book //Engineering Design via Surrogate Modelling: A Practical Guide// by Dr. Alexander Forrester, Dr. Andras Sobester, Prof. Andy Keane. The code is split into the folders:
    * Sampling Plans
    * Constructing a Surrogate
    * Exploring and Exploiting a Surrogate
    * Advanced Concepts
    * Example Problems
    * Example Scripts

Link to the book description:

http://www.wiley.com//legacy/wileychi/forrester/

The functions are downloadable here:

http://www.personal.soton.ac.uk/aijf197/Website%20Code%20November%2010.zip



===== SCILAB Krisp toolbox =====
//NKO participants: Janis Janusevskis, Rodolphe Le Riche//

This toolbox implements kriging based regression (also known as Gaussian process regression) and optimization of deterministic simulators. The toolbox consists of two main components: 
  * Functions for creation of kriging model for deterministic or noisy data (correlation kernels, hyper-parameter estimation, prediction, cross-validation).
  * Methods implementing Efficient Global Optimization (EGO) for time consuming deterministic functions.

Full description and source code available here :

http://atoms.scilab.org/toolboxes/krisp/

===== STK: A Small (Matlab/GNU Octave) Toolbox for Kriging =====
//NKO participant: Julien Bect//

[[http://sourceforge.net/projects/kriging/|Download on Sourceforge]]

==== Features ====
  * Meant to be compatible with GNU Octave and Matlab(TM)
  * Provides a convenient research tool for working with kriging-based methods

===== Other toolboxes =====
Matlab toolbox:

SUMO: http://www.sumo.intec.ugent.be/?q=main

DACE: http://www2.imm.dtu.dk/~hbn/dace/

GPML: http://www.gaussianprocess.org/gpml/code/matlab/doc/

Kriging and gaussian processes in several languages (C, C++, Matlab, Python):

http://www.gaussianprocess.org/#code