tag:joss.theoj.org,2005:/papers/tagged/sensitivity%20analysis
Journal of Open Source Software
2024-03-09T11:15:15Z
Journal of Open Source Software
https://joss.theoj.org
tag:joss.theoj.org,2005:Paper/4636
2024-03-09T11:15:15Z
2024-03-10T00:01:20Z
konfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferences
accepted
0.4.0
2023-07-31 19:56:25 UTC
95
2024-03-09 11:15:15 UTC
9
2024
5779
Sarah
Narvaiz
University of Tennessee, Knoxville, Knoxville, TN, USA
Qinyun
Lin
University of Gothenburg, Gothenburg, SE
Joshua
M.
Rosenberg
University of Tennessee, Knoxville, Knoxville, TN, USA
Kenneth
A.
Frank
Michigan State University, East Lansing, MI, USA
Spiro
J.
Maroulis
Arizona State University, Tempe, AZ, USA
Wei
Wang
University of Tennessee, Knoxville, Knoxville, TN, USA
Ran
Xu
University of Connecticut, Hartford, CT, USA
10.21105/joss.05779
https://doi.org/10.5281/zenodo.10708094
R
https://joss.theoj.org/papers/10.21105/joss.05779.pdf
Sensitivity analysis, Causal inference
tag:joss.theoj.org,2005:Paper/4587
2023-10-30T21:01:26Z
2023-10-31T20:12:32Z
UQTestFuns: A Python3 library of uncertainty quantification (UQ) test functions
accepted
v0.4.0
2023-07-07 17:24:02 UTC
90
2023-10-30 21:01:26 UTC
8
2023
5671
Damar
Wicaksono
Center for Advanced Systems Understanding (CASUS) - Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany
0000-0001-8587-7730
Michael
Hecht
Center for Advanced Systems Understanding (CASUS) - Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany
0000-0001-9214-8253
10.21105/joss.05671
https://doi.org/10.5281/zenodo.10047512
Python
https://joss.theoj.org/papers/10.21105/joss.05671.pdf
test functions, benchmark, uncertainty quantification, metamodeling, surrogate modeling, sensitivity analysis, reliability analysis, rare event estimation
tag:joss.theoj.org,2005:Paper/4478
2023-09-15T12:06:34Z
2023-09-18T07:49:02Z
PyThia: A Python package for Uncertainty Quantification based on non-intrusive polynomial chaos expansions
accepted
v3.1.0
2023-05-10 13:36:21 UTC
89
2023-09-15 12:06:34 UTC
8
2023
5489
Nando
Hegemann
Physikalisch-Technische Bundesanstalt, Germany
0000-0003-3953-9006
Sebastian
Heidenreich
Physikalisch-Technische Bundesanstalt, Germany
0000-0002-1909-5770
10.21105/joss.05489
https://doi.org/10.5281/zenodo.8329459
Python
https://joss.theoj.org/papers/10.21105/joss.05489.pdf
polynomial chaos expansion, sensitivity analysis, regression, function approximation
tag:joss.theoj.org,2005:Paper/4218
2023-06-16T16:37:49Z
2023-06-17T09:15:49Z
Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses
accepted
V1.0.0
2023-02-17 13:28:46 UTC
86
2023-06-16 16:37:49 UTC
8
2023
5291
Marc
Schouler
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
0000-0002-3708-4135
Robert
Alexander
Caulk
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
0000-0001-5618-8629
Lucas
Meyer
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France, Industrial AI Laboratory SINCLAIR, EDF Lab Paris-Saclay, France
0000-0001-5386-5997
Théophile
Terraz
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
Christoph
Conrads
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
Sebastian
Friedemann
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
Achal
Agarwal
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
0000-0002-3216-4769
Juan
Manuel
Baldonado
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
Bartłomiej
Pogodziński
Institute of Bioorganic Chemistry Polish Academy of Sciences, Poznań Supercomputing and Networking Center
Anna
Sekuła
Institute of Bioorganic Chemistry Polish Academy of Sciences, Poznań Supercomputing and Networking Center
0000-0003-3524-3160
Alejandro
Ribes
Industrial AI Laboratory SINCLAIR, EDF Lab Paris-Saclay, France
Bruno
Raffin
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France
10.21105/joss.05291
https://doi.org/10.5281/zenodo.8046630
C, Fortran, Python
https://joss.theoj.org/papers/10.21105/joss.05291.pdf
supercomputing, sensitivity analysis, deep learning, distributed systems, orchestration
tag:joss.theoj.org,2005:Paper/3819
2022-10-14T16:13:22Z
2022-10-15T00:00:36Z
GSAreport: Easy to Use Global Sensitivity Reporting
accepted
v1.0.0
2022-08-15 10:46:50 UTC
78
2022-10-14 16:13:22 UTC
7
2022
4721
Van
Stein,
Bas
LIACS, Leiden University, The Netherlands
0000-0002-0013-7969
Elena
Raponi
Technical University of Munich, Germany
0000-0001-6841-7409
10.21105/joss.04721
https://doi.org/10.5281/zenodo.7191341
Python, Jupyter Notebook
https://joss.theoj.org/papers/10.21105/joss.04721.pdf
global sensitivity analysis, explainable ai
tag:joss.theoj.org,2005:Paper/3592
2022-09-05T11:14:39Z
2023-10-08T07:24:41Z
tipr: An R package for sensitivity analyses for unmeasured confounders
accepted
v0.4.1
2022-05-06 16:58:03 UTC
77
2022-09-05 11:14:39 UTC
7
2022
4495
Lucy
D\'Agostino
McGowan
Wake Forest University, USA
0000-0001-7297-9359
10.21105/joss.04495
https://doi.org/10.5281/zenodo.6958926
R
https://joss.theoj.org/papers/10.21105/joss.04495.pdf
statistics, epidemiology, sensitivity analyses, causal inference, confounding
tag:joss.theoj.org,2005:Paper/3674
2022-08-17T19:22:52Z
2022-08-18T00:01:30Z
GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia
accepted
v2.0.0
2022-06-24 10:15:50 UTC
76
2022-08-17 19:22:52 UTC
7
2022
4561
Vaibhav
Kumar
Dixit
Julia Computing
0000-0001-7763-2717
Christopher
Rackauckas
Julia Computing, Massachusetts Institute of Technology, Pumas-AI
0000-0001-5850-0663
10.21105/joss.04561
https://doi.org/10.5281/zenodo.6993162
Julia
https://joss.theoj.org/papers/10.21105/joss.04561.pdf
julia, global sensitivity analysis
tag:joss.theoj.org,2005:Paper/2697
2021-11-21T18:30:33Z
2021-11-22T00:01:47Z
ogs6py and VTUinterface: streamlining OpenGeoSys workflows in Python
accepted
v0.31
2021-05-31 10:02:05 UTC
67
2021-11-21 18:30:33 UTC
6
2021
3673
Jörg
Buchwald
Helmholtz Center for Environmental Research - UFZ, Leipzig, Germany, Technische Universität Bergakademie Freiberg, Germany
0000-0001-5174-3603
Olaf
Kolditz
Helmholtz Center for Environmental Research - UFZ, Leipzig, Germany, Technische Universität Dresden, Germany, TUBAF-UFZ Center for Environmental Geosciences, Germany
0000-0002-8098-4905
Thomas
Nagel
Technische Universität Bergakademie Freiberg, Germany, TUBAF-UFZ Center for Environmental Geosciences, Germany
0000-0001-8459-4616
10.21105/joss.03673
https://doi.org/10.5281/zenodo.5705727
Jupyter Notebook
https://joss.theoj.org/papers/10.21105/joss.03673.pdf
Python, physics, THMC, VTU, time-series, sensitivity analysis, uncertainty quantification, OpenGeoSys
tag:joss.theoj.org,2005:Paper/68
2017-01-10T00:00:00Z
2021-02-15T11:34:24Z
SALib: An open-source Python library for Sensitivity Analysis
accepted
v1.0.0
2016-10-11 16:13:19 UTC
9
2017-01-10 00:00:00 UTC
2
2017
97
Jon
Herman
University of California, Davis
0000-0002-4081-3175
Will
Usher
University of Oxford
0000-0001-9367-1791
10.21105/joss.00097
https://doi.org/10.5281/zenodo.233103
Python
https://joss.theoj.org/papers/10.21105/joss.00097.pdf
sensitivity analysis, uncertainty, variance-based, global sensitivity analysis, fractional factorial, Method of Morris
tag:joss.theoj.org,2005:Paper/59
2016-09-29T00:00:00Z
2021-02-15T11:34:26Z
Python Active-subspaces Utility Library
accepted
v 0.1
2016-09-19 20:33:28 UTC
5
2016-09-29 00:00:00 UTC
1
2016
79
Paul
Constantine
Colorado School of Mines, Golden, CO
0000-0003-3726-6307
Ryan
Howard
Colorado School of Mines, Golden, CO
Andrew
Glaws
Colorado School of Mines, Golden, CO
Zachary
Grey
Colorado School of Mines, Golden, CO
Paul
Diaz
University of Colorado Boulder, Boulder, CO
Leslie
Fletcher
None
10.21105/joss.00079
https://doi.org/10.5281/zenodo.158941
Python, Jupyter Notebook
https://joss.theoj.org/papers/10.21105/joss.00079.pdf
python, active subspaces, dimension reduction, uncertainty quantification, sensitivity analysis, surrogate modeling