Package: mixedsde 5.0

Charlotte Dion

mixedsde: Estimation Methods for Stochastic Differential Mixed Effects Models

Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function.

Authors:Charlotte Dion [aut, cre], Adeline Sansom [aut], Simone Hermann [aut]

mixedsde_5.0.tar.gz
mixedsde_5.0.tar.gz(r-4.5-noble)mixedsde_5.0.tar.gz(r-4.4-noble)
mixedsde_5.0.tgz(r-4.4-emscripten)mixedsde_5.0.tgz(r-4.3-emscripten)
mixedsde.pdf |mixedsde.html✨
mixedsde/json (API)

# Install 'mixedsde' in R:
install.packages('mixedsde', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • neuronal.data - Trajectories Interspike Of A Single Neuron Of A Ginea Pig

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

1.70 score 7 scripts 200 downloads 21 exports 53 dependencies

Last updated 6 years agofrom:f90f5ac210. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linuxOKNov 21 2024

Exports:ad.propSdad.propSd_randomBayesianNormalbxchain2samplesdcCIR2diagnosticdiscreigenvaluesVEstParamNormallikelihoodNormallikelihoodNormalestimfixmixedsde.fitmixedsde.simmixture.simoutplot2comparepredprintUVvalid

Dependencies:ashbitopscliclustercolorspacedeSolvefansifarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrMASSMatrixmclustmgcvmisc3dmomentsmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigplot3DpracmaR6rainbowRColorBrewerRcppRCurlrlangscalessdetibbleutf8vctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Adaptation For The Proposal Variancead.propSd
Adaptation For The Proposal Variancead.propSd_random
S4 class for the Bayesian estimation resultsBayes.fit-class
S4 class for the Bayesian prediction resultsBayes.pred-class
Bayesian Estimation In Mixed Stochastic Differential EquationsBayesianNormal
Computation Of The Drift Coefficientbx
Removing Of Burn-in Phase And Thinningchain2samples
Likelihood Function For The CIR ModeldcCIR2
Calcucation Of Burn-in Phase And Thinning Ratediagnostic
Simulation Of Random Variablesdiscr
Matrix Of Eigenvalues Of A List Of Symetric MatriceseigenvaluesV
Maximization Of The Log Likelihood In Mixed Stochastic Differential EquationsEstParamNormal
S4 class for the frequentist estimation resultsFreq.fit-class
Computation Of The Log Likelihood In Mixed Stochastic Differential EquationslikelihoodNormal
Likelihood Function When The Fixed Effect Is EstimatedlikelihoodNormalestimfix
Estimation Of The Random Effects In Mixed Stochastic Differential Equationsmixedsde.fit
Simulation Of A Mixed Stochastic Differential Equationmixedsde.sim
Simulation Of A Mixture Of Two Normal Or Gamma Distributionsmixture.sim
Trajectories Interspike Of A Single Neuron Of A Ginea Pigneuronal.data
Transfers the class object to a listout
Plot method for the Bayesian estimation class objectplot,Bayes.fit,ANY-method
Plot method for the Bayesian prediction class objectplot,Bayes.pred,ANY-method
Plot method for the frequentist estimation class objectplot,Freq.fit,ANY-method
Comparing plot methodplot2compare
Comparing plot method plot2compare for three Bayesian estimation class objectsplot2compare,Bayes.fit-method
Comparing plot method plot2compare for three Bayesian prediction class objectsplot2compare,Bayes.pred-method
Prediction methodpred
Bayesian prediction method for a class object Bayes.fitpred,Bayes.fit-method
Prediction method for the Freq.fit class objectpred,Freq.fit-method
Print of acceptance rates of the MH stepsprint,Bayes.fit-method
Description of printprint,Freq.fit-method
Short summary of the results of class object Bayes.fitsummary,Bayes.fit-method
Short summary of the results of class object Freq.fitsummary,Freq.fit-method
Computation Of The Sufficient StatisticsUV
Validation of the chosen model.valid
Validation of the chosen model.valid,Bayes.fit-method
Validation of the chosen model.valid,Freq.fit-method