Package: GPoM 1.4

Mireille Huc

GPoM: Generalized Polynomial Modelling

Platform dedicated to the Global Modelling technique. Its aim is to obtain ordinary differential equations of polynomial form directly from time series. It can be applied to single or multiple time series under various conditions of noise, time series lengths, sampling, etc. This platform is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects Crops'IChaos and Musc & SlowFast). The method is described in the article : Mangiarotti S. and Huc M. (2019) <doi:10.1063/1.5081448>.

Authors:Sylvain Mangiarotti [aut], Mireille Huc [cre, aut], Flavie Le Jean [ctb], Malika Chassan [ctb], Laurent Drapeau [ctb], Institut de Recherche pour le Développement [fnd], Centre National de la Recherche Scientifique [fnd]

GPoM_1.4.tar.gz
GPoM_1.4.tar.gz(r-4.7-any)GPoM_1.4.tar.gz(r-4.6-any)
GPoM_1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GPoM/json (API)

# Install 'GPoM' in R:
install.packages('GPoM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • allMod_nVar3_dMax2 - Numerical description of a list of eighteen three-dimensional chaotic sytems
  • allToTest - A list providing the description of six models tested by the function 'autoGPoMoTest'.
  • data_vignetteIII - Output of the vignette 'III_Modelling'
  • data_vignetteVI - Output of the vignette 'VI_Sensitivity'
  • data_vignetteVII - Output of the vignette 'VII_Retro-Modelling'
  • NDVI - A time series of vegetation index measured from satellite
  • P1FxCh - A data set for testing periodicity
  • P1FxChP2 - A data set for testing periodicity
  • Ross76 - Time series of the Rossler-1976 system
  • RosYco - Twelve Rossler-1976 time series
  • svrlTS - A data set for the global modeling of time series in association
  • TS - Time series resulting from the integration of a non stationary system
  • TSallMod_nVar3_dMax2 - Time series of three-dimensional chaotic sytems

On CRAN:

Conda:

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

3.35 score 28 scripts 340 downloads 28 exports 30 dependencies

Last updated from:30d30bc8ef. Checks:2 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING147
source / vignettesOK303
linux-release-x86_64WARNING152
wasm-releaseOK143

Exports:autoGPoMoSearchautoGPoMoTestbDrvFiltcano2McombiEqcompDerivconcatconcatMulTSd2pMaxderivODEwMultiXdrvSuccfindAllSetsgloMoIdgPoMonumicanonumiMultiXnuminoisyp2dMaxpoLabspredictabpTimEvregOrdregSeriessubSysDtestPvisuEqvisuOutGPwInProd

Dependencies:base64encbslibcachemclideSolvedigestevaluatefastmapfloatfontawesomefshighrhtmltoolshtmlwidgetsjquerylibjsonliteknitrlifecyclemagrittrmemoisemimeR6rappdirsrglrlangrmarkdownsasstinytexxfunyaml

GPoM : 3 Modelling
Global Modelling | Single time series modelling | Detection of causal couplings and retro-modelling | Generalized global modelling and polynomial a priori structure | Blind separation and modelling of two independant sets of equations | Time series with gaps | Time series in associassion | Output visualization and global models validation
Rendered fromb3_Modelling.Rmdusingknitr::rmarkdown

Last update: 2023-06-16
Started: 2018-07-26

GPoM : 1 Conventions
Conventions used to describe a polynomial | Definition of a set of polynomial ODE | Numerical integration | Next steps
Rendered fromb1_Conventions.Rmdusingknitr::rmarkdown

Last update: 2020-02-18
Started: 2018-07-26

GPoM : 2 PreProcessing
Pre-processing for global modelling | Single time series | Multiple time series | Conclusion and next step
Rendered fromb2_PreProcessing.Rmdusingknitr::rmarkdown

Last update: 2020-02-18
Started: 2018-07-26

GPoM : 4 Visualization of the outputs
Model vizualization | Next step
Rendered fromb4_VisualizeOutputs.Rmdusingknitr::rmarkdown

Last update: 2020-02-18
Started: 2018-07-26

GPoM : 5 Models predictability
Model performances | Predictability | Conclusions
Rendered fromb5_Predictability.Rmdusingknitr::rmarkdown

Last update: 2020-02-18
Started: 2018-07-26

GPoM : 6 Approach sensitivity
Approach sensitivity | Sensitivity to the initial conditions | The original system and data | Model selection | Results | Sensitivity to signal length | Data | Global modelling | Sensitivity to subsampling and resampling | Subsampled time series | Resampled time series | Sensitivity to measurement noise (after smoothing) | Conclusions
Rendered fromb6_Sensitivity.Rmdusingknitr::rmarkdown

Last update: 2020-02-18
Started: 2018-07-26

GPoM : 7 Retro-modelling
Detection of miscellaneous chaotic systems | The Nosé-Hoover-1986 system | Data | Global modelling | The Genesio-Tesi system (1992) | The Sprott systems | The Spott-F system | The Spott-H system | The Spott-K system | The Spott-O system | The Spott-P system | The Spott-G system | The Spott-M system | The Spott-Q system | The Spott-S system | The Lorenz-1963 system | The Burke and Shaw system (1981) | The Lorenz-1984 system | The Chlouverakis-Sprott system (2004) | The Li system (2007) | The Cord system (Aguirre & Letellier 2012) | Conclusion
Rendered fromb7_Retro-modelling.Rmdusingknitr::rmarkdown

Last update: 2020-02-18
Started: 2018-07-26

GPoM : General introduction
Generalized Global Polynomial Modelling (GPoM)
Rendered froma_GeneralIntro.Rmdusingknitr::rmarkdown

Last update: 2020-02-18
Started: 2018-07-26

Readme and manuals

Help Manual

Help pageTopics
GPoM package: Generalized Polynomial ModellingGPoM-package
Numerical description of a list of eighteen three-dimensional chaotic sytems (see vignette '7_Retro-Modelling')allMod_nVar3_dMax2 allMod_nVar3_dMax2 data set
A list providing the description of six models tested by the function 'autoGPoMoTest'.allToTest
Automatic search of polynomial EquationsautoGPoMoSearch
Tests the numerical integrability of models and classify their dynamical regimeautoGPoMoTest
Builds the derivative filterbDrvFilt
cano2M : Converts a model in canonical form into a matrix formcano2M
combiEq : Combine Equations from different sourcescombiEq
Computes the successive derivatives of a time seriescompDeriv
Concat Concatenates separated time seriesconcat
ConcatMulTS Concatenates separated time series (of single or multiples variables)concatMulTS
Provides the number of polynomial terms 'pMax' given 'dMax' and 'nVar'd2pMax
Output of the vignette 'III_Modelling'data_vignetteIII data_vignetteIII data set
Output of the vignette 'VI_Sensitivity'data_vignetteVI data_vignetteVI data set
Output of the vignette 'VII_Retro-Modelling'data_vignetteVII data_vignetteVII data set
A subfonction for the numerical integration of polynomial equations provided in a generic form following the convetion defined by function 'poLabs'.derivODE2
deriveODEwMultiX : A Subfonction for the numerical integration of polynomial equations in the generic form defined by function 'poLabs' and with External Forcing F(t)derivODEwMultiX
Detection of limit cycles of period-1detectP1limCycl
drvSucc : Computes the successive derivatives of a time seriesdrvSucc
extractEq : Extracts Equations from one systemextractEq
Find all possible sets of equation combinations considering an ensemble of possible equation.findAllSets
Global Model IdentificationgloMoId
Generalized Polynomial ModelinggPoMo
Gram-Schmidt procedureGSproc
A time series of vegetation index measured from satelliteNDVI
Numerical Integration of models in ODE of polynomial formnumicano
Numerical Integration polynomial ODEs with Multiple eXternal forcingnumiMultiX
Generates time series of deterministic-behavior with stochatic perturbations (measurement and/or dynamical noise)numinoisy
For the numerical integration of ordinary differential equations with dynamical noise.odeBruitMult2
A data set for testing periodicityP1FxCh
A data set for testing periodicityP1FxChP2
p2dMax : provides the maximum polynomial degree 'dMax' given the number of variables 'nVar' and the number of possible polynomial terms 'pMax'.p2dMax
For parameter IdentificationparamId
Polynomial labels orderpoLabs
Estimate the models performance obtained with 'GPoMo' in term of predictabilitypredictab
Model stationnary testingpTimEv
Generate the conventional order for polynomial terms in a the polynomial formulationregOrd
Estimates the monomial time seriesregSeries
Time series of the Rossler-1976 systemRoss76 Rossler-1976 data set
Twelve Rossler-1976 time series (exclusively variable y)RosYco
subSysD : Sub-systems DisentanglingsubSysD
A data set for the global modeling of time series in associationsvrlTS
Periodic solution testtestP
Time series resulting from the integration of a non stationary systemTS
Time series of three-dimensional chaotic sytems (for vignette 'VII_Retro-Modelling')TSallMod_nVar3_dMax2 TSallMod_nVar3_dMax2 data set
Displays the models EquationsvisuEq
visuOutGP : get a quick information of gPoMo outputvisuOutGP
Weighted inner productwInProd