List of uncertainty propagation software
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List of uncertainty propagation software used for performing uncertainty propagation calculations:
- ASUE This is a powerful web interface powered by webMathematica for evaluating uncertainty symbolically using GUM. Besides, the webpage also allows symbolic uncertainty evaluation using ASUE framework (with reference), which is an extension to GUM framework
- Uncertainty Calculator This is a web interface for uncertainty calculations.
- Mathos Core Library Uncertainty package Open source (.NET targeting library).
- Error Propagation Calculator Free cross-platform calculator (OSX/Windows/Linux) written in Python. Essentially a GUI interface for the python Uncertainties library. Very easy to use and install.
- Error Calculator Free/libre cross-platform calculator with minimalistic interface. Designed for use in practical courses at natural sciences. Exposes all formulae needed to calculate the results, interoperability with office, support for physical quantities with units.
- Dempster Shafer with Intervals (DSI) Toolbox is a MATLAB toolbox for verified computing under Dempster–Shafer theory. It provides aggregation rules, fast (non-)monotonic function propagation, plots of basic probability assignments, verified Fault tree analysis, and much more.
- EasyGraph is a graphing package that supports error propagation directly into the error bars.
- Epc is an open source script based tool that calculates the propagation of errors in variables. To quote the text on the Epc web page "This is done by repeated calculation of the expression using variable-values which are generated using a random number generator whose mean and standard-deviation match the values specified for the variable".
- ErrorCalc is a scientific calculator app for iPhone or iPad that performs error propagation (Supports Algebraic and RPN modes of entry)
- GUM Workbench implements a systematic way to analyze an uncertainty problem for single and multiple results. GUM + Monte Carlo. Free restricted educational version available.
- GUM Tree is a design pattern for propagating measurement uncertainty. There is an implementation in R and add-ons for Excel (real and complex numbers).
- GUM Tree Calculator is a programmable Windows command-line tool with full support for uncertainty calculations involving real and complex quantities.
- The Gustavus propagator is an open source calculator that supports error propagation developed by Thomas Huber.
- Metas.UncLib is a C# software library. There is a wrapper for MATLAB. It supports: multivariate uncertainties, complex values, correlations, vector and matrix algebra.
- The laffers.net propagator is a web based tool for propagating errors in data. The tool uses the standard methods for propagation.
- MUSE Measurement Uncertainty Simulation and Evaluation using the monte carlo method.
- Uncertainties is a powerful free calculator and Python software library for transparently performing calculations with uncertainties and correlations.
- SOERP implements second-order error propagation as a free Python library. Calculations are carried out naturally in calculator format and correlations are maintained.
- Risk Calc supports probability bounds analysis, standard fuzzy arithmetic, and classical interval analysis for conducting distribution-free or nonparametric risk analyses.
- GUMsim is a Monte Carlo simulator and uncertainty estimator for Windows
- SCaViS is a free data-analyais program written in Java and supports Python and Groovy.
- QMSys GUM is a powerful commercial tool for measurement uncertainty analysis including Monte Carlo simulation for Windows (free restricted educational version available).
- MC-Ed is a native Windows software to perform uncertainty calculations according to the Supplement 1 to the Guide to the expression of uncertainty in measurement using Monte-Carlo method.
- OpenCOSSAN is a MATLAB toolbox for uncertainty propagation, reliability analysis, model updating, sensitivity and robust design optimization. Allows to interact with 3rd party solvers. Interfaces with HPC through GridEngine and OpenLava.
|Name||Creator||License||Programming language||Handles correlations?||Cross-platform?||Calculator?||Library?||Remarks|
|ASUE||TC-32 - Fault Tolerant Measurement Systems||Free||Mathematica||No||Yes||Yes||No||This is a powerful web interface powered by webMathematica for evaluating uncertainty symbolically using GUM. In addition, the webpage also allows symbolic uncertainty evaluation using ASUE framework (with reference), which is an extension to the GUM uncertainty propagation framework.|
|Error Propagation Calculator||R. Paul Nobrega||Free||Python||Yes||Yes||Yes||No||Desktop calculator (Windows/Mac/Linux), handles up to 26 variable and error pairs per computation. Evaluates native python expressions. No prior knowledge of python language is required for use. Windows installer includes python dependencies.|
|Abacus||Mischa Megens||Free||C, Win32||Yes||No||Yes||No||Desktop calculator (Windows), handles multiple expressions|
|App::ErrorCalculator||Steffen Müller||GNU GPL||Perl||No||Yes||Library and script to process tabular values|
|Dempster Shafer with Intervals (DSI) Toolbox||Gabor Rebner||Free for private and academic use||MATLAB||No||Yes||Verified computation of basic probability assignments and Fault tree analysis under Dempster–Shafer theory. Fast evaluation of (non-) monotonic system functions and aggregation rules.|
|Error Calculator||Gregor Bollerhey||GNU GPL||Python||Yes||Yes||Yes||Standalone notebook calculator, optimized for lab classes (displays formula, export to spreadsheet or latex). Supports physical quantities with units.|
|Experimental Data Analyst (EDA)||David Harrison||Proprietary||Mathematica||Yes||No||Yes||Library|
|EPC: error-propagating calculator||Dan Kelley||GNU GPL||Perl||Yes||Yes||Yes||Perl Script. Monte Carlo evaluation of an expression.|
|ErrorCalc iPhone/iPad calculator app||Thomas Huber||Proprietary||Unknown||No||No||Yes||No||Scientific calculator app for iPhone or iPad that performs error propagation (Algebraic and RPN Entry modes)|
|FuncDesigner||Dmitrey||BSD||Python||No||Yes||Yes||Library and stand-alone (via the Python shell). Involves Automatic differentiation (possibly large-scale sparse)|
|GUM Workbench||Metrodata GmbH||Proprietary||Delphi||Yes||Yes||No||Standalone. Detailed consequences of a model equation. GUM + Monte Carlo. Free restricted educational version available.|
|GUM_MC||Jean-Marie Biansan||GNU GPL||Lazarus||Yes||Yes||Yes||No||Standalone. Gum framework and Monte Carlo method.|
|fussy||S. Bhatnagar||Free but copyrighted||C||Yes||No||Yes||Yes||Scripting language called 'fussy', similar to C.|
|Gustavus Adolphus error propagation calculator||Thomas Huber||GNU GPL||Unknown||No||No||Yes||No||Executable only. Desk calculator style (with no expression parentheses).|
|Measurement Software Toolkit||Industrial Research Ltd||Free for non-commercial use||R; Excel plug-in||Yes||Yes||Yes||Library and plug-in|
|GUM Tree Calculator (GTC)||Industrial Research Ltd||Single-user, non-transferable. Free.||Python||Yes||No||Yes||No||A programmable command-line calculator for Windows. Suitable for calculations involving real and complex quantities. Programmable in Python. An IDE with syntax highlighting and on-line help is included.|
|MUSE||Measurement Uncertainty Research Group, ETH Zürich||Free for non-commercial use||C++||Yes||Yes||No||Standalone. Monte-Carlo sampling. Interprets an XML model description file.|
|Metas.UncLib||Michael Wollensack, METAS||Free use; redistribution prohibited||C#; MATLAB wrapper||Yes||No||Yes||Yes||Library, and command-line calculator (through Matlab)|
|Number::WithError||Steffen Müller||GNU GPL||Perl||No||No||Yes||Library|
|uncertainties||Eric O. Lebigot (EOL)||BSD||Python||Yes||Yes||Yes||Yes||Library and stand-alone command-line calculator (via the Python shell). Fully transparent calculations with correlations. Also handles matrices with uncertainties. Automatically calculates all the derivatives of an expression|
|soerp||Abraham Lee||BSD||Python||Yes||Yes||Yes||Yes||Free Python library and command-line calculator. Fully transparent second-order calculations with correlations. Automatically calculates all the first and second derivatives of an expression using the free Python package ad|
|Risk Calc||Scott Ferson||Proprietary||C++||Yes||Yes||Probabilistic and interval uncertainty. Also handles uncertainty about correlations.|
|propagate||Andrej-Nikolai Spiess||GNU GPL||R||Yes||Yes||No||Yes||An R package that conducts error propagation by first- and second-order Taylor approximation (GUM 2008) and Monte-Carlo simulation (GUM 2008 S1), using full covariance structure.|
|GUMsim||QuoData GmbH||Proprietary||Delphi||Yes||No||Yes||No||Standalone. Detailed consequences of a model equation.|
|SCaViS||JWork.ORG||GNU GPL||Java, Python||Yes||Yes||Yes||Yes||Standalone. Conducts error propagation by first- and second-order Taylor approximation and using a Monte Carlo approach for complex functions|
|QMSys GUM||Qualisyst Ltd.||Proprietary||Unknown||Yes||No||Standalone. Linear/nonlinear models, Monte Carlo method. (free restricted educational version available)|
|MC-Ed||Florian Platel (MetGen)||Free||Embarcadero Delphi||No||No||Yes||No||Desktop calculator (Win32 native application). Monte-Carlo simulations.|
|OpenCOSSAN||Institute of Risk and Uncertainty, University of Liverpool||GNU GPL||MATLAB||Yes||Yes||No||Yes||Provides an object-oriented programming interface to advanced algorithms and solution sequences.|
- List of Automatic differentiation software (also can be used to obtain uncertainties)
- Y. C. Kuang, A. Rajan, M. P.-L. Ooi, and T. C. Ong (2014), "Standard uncertainty evaluation of multivariate polynomial," Measurement, vol. 58, pp. 483-494, Dec. 2014
- A. Rajan, M. P. Ooi, Y. C. Kuang, and S. N. Demidenko, "Analytical Standard Uncertainty Evaluation Using Mellin Transform," Access, IEEE, vol. 3, pp. 209-222, 2015.
- Auer, E., Luther, W., Rebner, G., Limbourg, P. (2010) A Verified MATLAB Toolbox for the Dempster-Shafer Theory. Proceedings of the Workshop on the Theory of Belief Functions
- Bevington, P.R. and Robinson, D.K. (2002) Data Reduction and Error Analysis for the Physical Sciences, 3rd Ed., McGraw-Hill ISBN 0-07-119926-8
- Ferson, S., Kreinovich, V., Hajagos, J., Oberkampf, W. and Ginzburg, L. (2007) "Experimental Uncertainty Estimation and Statistics for Data Having Interval Uncertainty". Sandia National Laboratories Report: SAND2007-0939.
- Patelli, E., An Open Computational Framework for Reliability Based Optimization, In proceeding of: The Eleventh International Conference on Computational Structures Technology, Dubrovnik, Croatia 4-7 September 2012