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 <[email protected]>, Matieyendou Lamboni <[email protected]>, Hervé Monod <[email protected]>

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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

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

2.38 score 1 stars 24 scripts 220 downloads 1 mentions 30 exports 84 dependencies

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

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-linuxNOTENov 10 2024

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 Nov 10 2024.

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

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