Package: multisensi 2.1-1

Hervé Monod

multisensi: Multivariate Sensitivity Analysis

Functions to perform sensitivity analysis on a model with multivariate output.

Authors:Caroline Bidot <caroline.bidot@inra.fr>, Matieyendou Lamboni <matieyendou.lamboni@gmail.com>, Hervé Monod <herve.monod@inra.fr>

multisensi_2.1-1.tar.gz
multisensi_2.1-1.tar.gz(r-4.5-noble)multisensi_2.1-1.tar.gz(r-4.4-noble)
multisensi_2.1-1.tgz(r-4.4-emscripten)multisensi_2.1-1.tgz(r-4.3-emscripten)
multisensi.pdf |multisensi.html
multisensi/json (API)
NEWS

# Install 'multisensi' in R:
install.packages('multisensi', repos = 'https://cloud.r-project.org')
Datasets:
  • Climat - Climate data
  • biomasseX - A factorial input design for the main example
  • biomasseY - Output of the biomasse model for the plan provided in the package

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 1 stars 305 downloads 1 mentions 30 exports 84 dependencies

Last updated 7 years agofrom:b5403a8150. Checks:1 OK, 2 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-linuxNOTEMar 10 2025
R-4.4-linuxNOTEMar 10 2025

Exports:analysis.anoasganalysis.sensitivitybasis.ACPbasis.bsplinesbasis.minebasis.osplinesbasis.polybiomassebsplinedynsigraph.bargraph.pcgrpe.gsigsimultisensimultivarplanfactplanfact.asplot.dynsiplot.gsipredict.gsiprint.dynsiprint.gsiqualitysesBsplinesNORMsesBsplinesORTHONORMsimulmodelsummary.dynsisummary.gsiyapprox

Dependencies:base64encbootbslibcachemclasscliclueclustercodetoolscolorspacecommonmarkcrayondigestdplyrdtwdtwclustevaluatefansifarverfastmapflexclustfontawesomeforeachfsgenericsggplot2ggrepelgluegtablehighrhtmltoolshttpuvisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemodeltoolsmunsellnlmenumberspillarpkgconfigplyrpromisesproxyR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppThreadreshape2rlangRSpectrasassscalessensitivityshinyshinyjssourcetoolsstringistringrtibbletidyselectutf8vctrsviridisLitewithrxfunxtableyaml

Quick guide

Rendered frommultisensi-vignette.rnwusingknitr::knitron Mar 10 2025.

Last update: 2018-04-10
Started: 2016-04-27

Citation

'multisensi' is an R package to perform sensitivity analysis on a model with multivariate output. The methodology is described and illustrated in the following two papers. If you are using 'multisensi' for research, please acknowledge at least one of them in your publications:

Matieyendou Lamboni, David Makowski, Simon Lehuger, Benoit Gabrielle and Hervé Monod (2009). Multivariate global sensitivity analysis for dynamic crop models. Field Crops Research, 113, 312-320.

Matieyendou Lamboni, Hervé Monod, and David Makowski (2011). Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Reliability Engineering and System Safety, 96, 450-459.

Bidot C, Lamboni M, Monod H (2018). multisensi: Multivariate Sensitivity Analysis. R package version 2.1-1, https://CRAN.R-project.org/package=multisensi.

Corresponding BibTeX entries:

  @Article{,
    author = {Matieyendou Lamboni and David Makowski and Simon Lehuger
      and Benoit Gabrielle and Hervé Monod},
    title = {Multivariate global sensitivity analysis for dynamic crop
      models},
    journal = {Field Crops Research},
    year = {2009},
    volume = {113},
    pages = {-8},
  }
  @Article{,
    author = {Matieyendou Lamboni and Hervé Monod and David Makowski},
    title = {Multivariate sensitivity analysis to measure global
      contribution of input factors in dynamic models},
    journal = {Reliability Engineering and System Safety},
    year = {2011},
    volume = {96},
    pages = {-9},
  }
  @Manual{,
    title = {{multisensi}: Multivariate Sensitivity Analysis},
    author = {Caroline Bidot and Matieyendou Lamboni and Hervé Monod},
    year = {2018},
    note = {R package version 2.1-1},
    url = {https://CRAN.R-project.org/package=multisensi},
  }

Readme and manuals

Help Manual

Help pageTopics
Multivariate sensitivity Analysismultisensi-package
Runs a series of analyses of varianceanalysis.anoasg
Runs a series of sensitivity analyses by a function from the 'sensitivity' packageanalysis.sensitivity
A function to decompose multivariate data by principal components analysis (PCA)basis.ACP
A function to decompose multivariate data on a B-spline basisbasis.bsplines
A function to decompose multivariate data on a user-defined basisbasis.mine
A function to decompose multivariate data on an orthogonal B-spline basis (O-spline)basis.osplines
A function to decompose multivariate data on a polynomial basisbasis.poly
The Winter Wheat Dynamic Modelbiomasse
A factorial input design for the main examplebiomasseX
Output of the biomasse model for the plan provided in the packagebiomasseY
function to evaluate B-spline basis functionsbspline
Climate dataClimat
Dynamic Sensitivity Indices: DSIdynsi
Sensitivity index bar plotgraph.bar
Principal Components graph for gsi objectsgraph.pc
Group factor GSI, obsolete functiongrpe.gsi
Generalised Sensitivity Indices: GSIgsi
A function with multiple options to perform multivariate sensitivity analysismultisensi
A function to decompose the output data set and reduce its dimensionmultivar
Complete factorial design in lexical orderplanfact
Complete factorial designplanfact.as
Plot method for dynamic sensitivity resultsplot.dynsi
Plot method for generalised sensitivity analysisplot.gsi
A function to predict multivariate outputpredict.gsi
print DYNSIprint.dynsi
print GSIprint.gsi
quality of any approximationquality
normalized B-splines basis functionssesBsplinesNORM
orthogonalized B-splines basis functionssesBsplinesORTHONORM
Model simulationsimulmodel
dynsi summarysummary.dynsi
summary of GSI resultssummary.gsi
Prediction based on PCA and anovas (NOT ONLY)yapprox