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  • Opus Forum
    contributions Scripts articles commentaries concerning a method or its software development as well as preprints are typical ways to contribute at the Forum level Apart from adding features the contributor can also provide test cases usually industrial examples showing the way in which OPUS methods and tools can be used to solve a real life problem While not a software contribution this form of contribution is both important and precious

    Original URL path: http://www.opus-project.fr/index.php/contributionmain/73-opusforum?tmpl=component&print=1&layout=default&page= (2016-01-11)
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  • OPUS
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    Original URL path: http://www.opus-project.fr/index.php/component/mailto/?tmpl=component&link=aHR0cDovL3d3dy5vcHVzLXByb2plY3QuZnIvaW5kZXgucGhwL2NvbnRyaWJ1dGlvbm1haW4vNzMtb3B1c2ZvcnVt (2016-01-11)
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  • Opus Contrib
    written in any of the following supported languages C Python R Scilab Matlab Octave The contribution s quality is the contributor s responsibility it is up to him her to ensure the quality of the code the respect of the programming rules as well as the mathematical correctness Different kinds of OPUS Contrib contribution types exist differentiated with respect to the integration with the others Integration the package is created using the OPUS module system basically a C module with the corresponding Python layer and written according to the relevant rules however the contributor chooses not to aim at OPUS Lib for a variety of reasons e g releasing some constraints due to programming quality Compatibility the package is compatible with the rest of OPUS i e written using a language or formalism allowing it to directly interact with OPUS This means either 1 a C code which can use all Lib and C Contrib features The code may be contributed with or without the corresponding Python layer 2 a Python script which can use all of the Lib and all installed Python features C Python features and Opus style modules from Contrib 3 a Scilab script using OPUS features

    Original URL path: http://www.opus-project.fr/index.php/contributionmain/72-opuscontrib?tmpl=component&print=1&layout=default&page= (2016-01-11)
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  • OPUS
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    Original URL path: http://www.opus-project.fr/index.php/component/mailto/?tmpl=component&link=aHR0cDovL3d3dy5vcHVzLXByb2plY3QuZnIvaW5kZXgucGhwL2NvbnRyaWJ1dGlvbm1haW4vNzItb3B1c2NvbnRyaWI%3D (2016-01-11)
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  • Opus Lib
    this the contribution needs to strictly follow a contribution checklist In a nutshell it must present the following features Functionality demonstrated in an academic and or R D environment possibly with a preliminary code proposed to the community for reviewing purpose System software architecture development initiated to include interoperability reliability maintainability extensibility scalability and security issues a high level of documentation both in and outside the code is also necessary

    Original URL path: http://www.opus-project.fr/index.php/contributionmain/71-opuslib (2016-01-11)
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  • Contributions
    to use existing objects Development when the conception is done the development starts It consists in the implementation of the objects and algorithms Opus has a set of development rules that should be followed in all cases but must be followed if the contribution is to be integrated at the Opus Lib level See the Programming Rules guide and following articles for more detail Test cases and unitary validation this step is required in order to demonstrate that the development is correct Each object and methods should be tested and validated with comparisons with analytical methods or reference methods Documentation of the development a documentation of the development is required in order to secure the development and its maintainability There are several types of documentation Reference documentation it contains the mathematical basis of the method developed It is based on the structured mathematical description Architecture documentation it describes the architecture of the different objects developed and their link with the existing version of the library User manual it contains the description of the objects the inputs and outputs of their methods Use case guide it contains a simple use case of the method There are three levels of contribution in Opus detailed in the following articles Opus Lib Basically contributing to the Opus lib means contributing to the core of the software Details Opus Contrib the set of all packages added to Opus that are not a part of Opus Lib Details Opus Forum this website provides among other things a forum available to the community for the purpose of discussing planning or proposing Details Test cases while not a contribution level per se industrial test cases are important additions to the project Details Last Updated Tuesday 05 January 2010 01 45 Contribution Overview Monday 04 January 2010 21 38 Author Administrator This article aims at presenting the various ways in which one can contribute to the Opus platform In a few words Opus is an uncertainty treatment platform giving access to Many advanced mathematical features The possibility of easily interacting with big codes via wrappers The availability of different languages to access or add to the features C Python R Scilab that is either compiled code or scripts A community of cutting edge scientists from many prestigious entities see consortium members built around a website and forum The possibility for the users to provide features for the software contributions Opus is built on the basis of the opensource software Openturns which is also an uncertainty treatment platform The next article describes the various levels of integration at which one can contribute based on the architecture In a few words there are three levels of integration from least to most integrated Opus Forum The contributor uses the forum space available in the OPUS web site to discuss and propose features or ideas in a completely free way from a simple algorithm to a bit of code or even a complete module albeit incompatible with other OPUS contributions Scripts articles commentaries

    Original URL path: http://www.opus-project.fr/index.php/contributionmain?start=5 (2016-01-11)
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  • Contributions
    also uses The Opus project created new features for Openturns especially a Polynomial Chaos Expansion PCE library This is now used on real industrial cases on a regular basis and is available for the whole community for business academic or teaching purposes See website and blog for details This is a joint work of EADS EDF and Phiméca The contribution of EADS to these developments has been mainly funded by OPUS while the contribution of EDF and Phiméca has been funded by own resources and by the ANR project MIRADOR Modélisation interactive des risques associés au développement d ouvrages robustes ref ANR 06 RGCU 008 NISP toolbox for Scilab The goal of this toolbox is to provide a tool to manage uncertainties in simulated models for the Scilab platform This Scilab toolbox is based on the NISP C library where NISP stands for Non Intrusive Spectral Projection The NISP library is based on a set of 3 C classes so that it provides an object oriented framework for uncertainty analysis The Scilab toolbox provides a pseudo object oriented interface to this library so that the two approaches are consistent See wiki for details Opus Contrib Octave Matlab toolbox for kriging a toolbox for carrying out kriging regression in Matlab also compatible with Octave with some limitations See website on sourceforge for more details Various R scripts for inverse probabilistic modeling were created dealing with MCMC estimation usecase scripts The idea here was to develop the scripts applied to a usecase while reusing existing tools based on the R language when relevant Two R packages available on the CRAN site have been identified and both packages may be used conjointly with the R package CODA which allows output analysis and diagnostics for Markov Chain Monte Carlo simulations These packages are MCMC package and MCMCpack package MLE estimation scripts ECME Expectation Conditional Maximization Either by iterated linearizations to deal with non linear cases based on G Celeux et al Identifying intrinsic variability in multivariate systems through linearised inverse methods Rapport de recherche INRIA RR 6400 2007 S A EM Stochastic Approximation version of Expectation Maximization which allows to perform SEM or SAEM Relevant publications to SA E M G Celeux and J Diebolt The SEM Algorithm a Probabilistic 101 128 ANR OPUS Final Report D WP0 11 03 A Teacher Algorithm Derived from the EM Algorithm for the Mixture Problem Computational Statistics Quaterly 2 73 82 1985 and E Kuhn Estimation par maximum de vraisemblance dans des problèmes inverses non linéaires Mémoire de thèse Université Paris XI d Orsay 2003 Opus Forum High quantile estimation by Multi Element Polynomial Chaos expansions This contribution comes mainly from the work of the post doc Jordan Ko Paris Diderot It is a brief summary of a paper submitted to Journal of Computational Physics Functional sensitivity analysis variable selection using varying coefficient modeling and application to diesel engine smoke depollution This contribution came mainly from the works of the DICE consortium It is thus a contribution

    Original URL path: http://www.opus-project.fr/index.php/contributionmain?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)
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  • Contributions
    Opus also uses The Opus project created new features for Openturns especially a Polynomial Chaos Expansion PCE library This is now used on real industrial cases on a regular basis and is available for the whole community for business academic or teaching purposes See website and blog for details This is a joint work of EADS EDF and Phiméca The contribution of EADS to these developments has been mainly funded by OPUS while the contribution of EDF and Phiméca has been funded by own resources and by the ANR project MIRADOR Modélisation interactive des risques associés au développement d ouvrages robustes ref ANR 06 RGCU 008 NISP toolbox for Scilab The goal of this toolbox is to provide a tool to manage uncertainties in simulated models for the Scilab platform This Scilab toolbox is based on the NISP C library where NISP stands for Non Intrusive Spectral Projection The NISP library is based on a set of 3 C classes so that it provides an object oriented framework for uncertainty analysis The Scilab toolbox provides a pseudo object oriented interface to this library so that the two approaches are consistent See wiki for details Opus Contrib Octave Matlab toolbox for kriging a toolbox for carrying out kriging regression in Matlab also compatible with Octave with some limitations See website on sourceforge for more details Various R scripts for inverse probabilistic modeling were created dealing with MCMC estimation usecase scripts The idea here was to develop the scripts applied to a usecase while reusing existing tools based on the R language when relevant Two R packages available on the CRAN site have been identified and both packages may be used conjointly with the R package CODA which allows output analysis and diagnostics for Markov Chain Monte Carlo simulations These packages are MCMC package and MCMCpack package MLE estimation scripts ECME Expectation Conditional Maximization Either by iterated linearizations to deal with non linear cases based on G Celeux et al Identifying intrinsic variability in multivariate systems through linearised inverse methods Rapport de recherche INRIA RR 6400 2007 S A EM Stochastic Approximation version of Expectation Maximization which allows to perform SEM or SAEM Relevant publications to SA E M G Celeux and J Diebolt The SEM Algorithm a Probabilistic 101 128 ANR OPUS Final Report D WP0 11 03 A Teacher Algorithm Derived from the EM Algorithm for the Mixture Problem Computational Statistics Quaterly 2 73 82 1985 and E Kuhn Estimation par maximum de vraisemblance dans des problèmes inverses non linéaires Mémoire de thèse Université Paris XI d Orsay 2003 Opus Forum High quantile estimation by Multi Element Polynomial Chaos expansions This contribution comes mainly from the work of the post doc Jordan Ko Paris Diderot It is a brief summary of a paper submitted to Journal of Computational Physics Functional sensitivity analysis variable selection using varying coefficient modeling and application to diesel engine smoke depollution This contribution came mainly from the works of the DICE consortium It is thus a

    Original URL path: http://www.opus-project.fr/index.php/contributionmain?el_mcal_month=2&el_mcal_year=2016 (2016-01-11)
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