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- Contribution process

conception Functional identification following a need emerging from the case studies the method to be implemented is identified Complete mathematical description of the method for this part the references are found Among all the sources the most obvious are scientific articles and books The references should cover the whole algorithms and concepts involved in the development of the function Structured mathematical description this document describes the mathematical process followed by the method This description is self sufficient homogeneous in terms of notations and as structured as possible to make the emergence of concepts as easy as possible The software conception is based on this document Software conception from the structured mathematical description it is then easier to choose the objects that are implemented It highlights the services attached to the objects the dependencies with other objects and or the possibility 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

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

Open archived version from archive - How to contribute

see the Contribution Guide This article also makes the assumption that the contribution is ready from a mathematical point of view that is the mathematical method and algorithms are well described ideally validated on a laboratory test case or at least on a sandbox example Contribution Checklist Identify existing features and necessary developments Choose contribution level Choose language type Write the specifications functional and technical specifications Develop the contribution classes

Original URL path: http://www.opus-project.fr/index.php/contributionmain/howtocontribute?tmpl=component&print=1&page= (2016-01-11)

Open archived version from archive - How to contribute

language see the Contribution Guide This article also makes the assumption that the contribution is ready from a mathematical point of view that is the mathematical method and algorithms are well described ideally validated on a laboratory test case or at least on a sandbox example Contribution Checklist Identify existing features and necessary developments Choose contribution level Choose language type Write the specifications functional and technical specifications Develop the contribution

Original URL path: http://www.opus-project.fr/index.php/contributionmain/howtocontribute?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)

Open archived version from archive - How to contribute

available language see the Contribution Guide This article also makes the assumption that the contribution is ready from a mathematical point of view that is the mathematical method and algorithms are well described ideally validated on a laboratory test case or at least on a sandbox example Contribution Checklist Identify existing features and necessary developments Choose contribution level Choose language type Write the specifications functional and technical specifications Develop the

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

Open archived version from archive - Available contributions

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

Original URL path: http://www.opus-project.fr/index.php/contributionmain/availablecontributions?tmpl=component&print=1&page= (2016-01-11)

Open archived version from archive - Available contributions

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

Original URL path: http://www.opus-project.fr/index.php/contributionmain/availablecontributions?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)

Open archived version from archive - Available contributions

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

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

Open archived version from archive - Joining the consortium

Author Administrator Joining the consortium When your involvment with Opus reaches a certain level typically if you wish to provide a Lib level contribution you might want to think about joining the consortium In this case please contact the project

Original URL path: http://www.opus-project.fr/index.php/contributionmain/joiningtheconsortium?tmpl=component&print=1&page= (2016-01-11)

Open archived version from archive