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.

Install the latest version of this package by entering the following in R:
install.packages("mixedsde")
AuthorCharlotte Dion [aut, cre], Adeline Sansom [aut], Simone Hermann [aut]
Date of publication2016-07-12 07:49:43
MaintainerCharlotte Dion <charlotte.dion1@gmail.com>
LicenseGPL (>= 2)
Version2.0
https://cran.r-project.org/package=mixedsde

View on CRAN

Man pages

ad.propSd: Adaptation For The Proposal Variance

ad.propSd_random: Adaptation For The Proposal Variance

Bayes.fit-class: S4 class for the Bayesian estimation results

BayesianNormal: Bayesian Estimation In Mixed Stochastic Differential...

Bayes.pred-class: S4 class for the Bayesian prediction results

bx: Computation Of The Drift Coefficient

chain2samples: Removing Of Burn-in Phase And Thinning

dcCIR2: Likelihood Function For The CIR Model

diagnostic: Calcucation Of Burn-in Phase And Thinning Rate

discr: Simulation Of Random Variables

eigenvaluesV: Matrix Of Eigenvalues Of A List Of Symetric Matrices

EstParamNormal: Maximization Of The Log Likelihood In Mixed Stochastic...

Freq.fit-class: S4 class for the frequentist estimation results

likelihoodNormal: Computation Of The Log Likelihood In Mixed Stochastic...

likelihoodNormalestimfix: Likelihood Function When The Fixed Effect Is Estimated

mixedsde.fit: Estimation Of The Random Effects In Mixed Stochastic...

mixedsde-package: Density estimation in mixed stochastic differential models

mixedsde.sim: Simulation Of A Mixed Stochastic Differential Equation

mixture.sim: Simulation Of A Mixture Of Two Normal Or Gamma Distributions

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

out: Transfers the class object to a list

plot2compare: Comparing plot method

plot2compare-Bayes.fit-method: Comparing plot method plot2compare for three Bayesian...

plot2compare-Bayes.pred-method: Comparing plot method plot2compare for three Bayesian...

plot-Bayes.fit-ANY-method: Plot method for the Bayesian estimation class object

plot-Bayes.pred-ANY-method: Plot method for the Bayesian prediction class object

plot-Freq.fit-ANY-method: Plot method for the frequentist estimation class object

pred: Prediction method

pred-Bayes.fit-method: Bayesian prediction method for a class object Bayes.fit

pred-Freq.fit-method: Prediction method for the Freq.fit class object

print-Bayes.fit-method: Print of acceptance rates of the MH steps

print-Freq.fit-method: Description of print

summary-Bayes.fit-method: Short summary of the results of class object Bayes.fit

summary-Freq.fit-method: Short summary of the results of class object Freq.fit

UV: Computation Of The Sufficient Statistics

valid: Validation of the chosen model.

valid-Bayes.fit-method: Validation of the chosen model.

valid-Freq.fit-method: Validation of the chosen model.

Functions

ad.propSd Man page
ad.propSd_random Man page
Bayes.fit-class Man page
BayesianNormal Man page
Bayes.pred-class Man page
bx Man page
chain2samples Man page
dcCIR2 Man page
diagnostic Man page
discr Man page
eigenvaluesV Man page
EstParamNormal Man page
Freq.fit-class Man page
likelihoodNormal Man page
likelihoodNormalestimfix Man page
mixedsde Man page
mixedsde.fit Man page
mixedsde-package Man page
mixedsde.sim Man page
mixture.sim Man page
neuronal.data Man page
out Man page
plot2compare Man page
plot2compare,Bayes.fit-method Man page
plot2compare,Bayes.pred-method Man page
plot,Bayes.fit,ANY-method Man page
plot,Bayes.pred,ANY-method Man page
plot,Freq.fit,ANY-method Man page
pred Man page
pred,Bayes.fit-method Man page
pred,Freq.fit-method Man page
print,Bayes.fit-method Man page
print,Freq.fit-method Man page
summary,Bayes.fit-method Man page
summary,Freq.fit-method Man page
UV Man page
valid Man page
valid,Bayes.fit-method Man page
valid,Freq.fit-method Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.