Estimation Methods for Stochastic Differential Mixed Effects Models
Adaptation For The Proposal Variance
Adaptation For The Proposal Variance
S4 class for the Bayesian estimation results
S4 class for the Bayesian prediction results
Bayesian Estimation In Mixed Stochastic Differential Equations
Computation Of The Drift Coefficient
Removing Of Burn-in Phase And Thinning
Likelihood Function For The CIR Model
Calcucation Of Burn-in Phase And Thinning Rate
Simulation Of Random Variables
Matrix Of Eigenvalues Of A List Of Symetric Matrices
Maximization Of The Log Likelihood In Mixed Stochastic Differential Eq...
S4 class for the frequentist estimation results
Computation Of The Log Likelihood In Mixed Stochastic Differential Equ...
Likelihood Function When The Fixed Effect Is Estimated
Estimation Of The Random Effects In Mixed Stochastic Differential Equa...
Simulation Of A Mixed Stochastic Differential Equation
Simulation Of A Mixture Of Two Normal Or Gamma Distributions
Transfers the class object to a list
Plot method for the Bayesian estimation class object
Plot method for the Bayesian prediction class object
Plot method for the frequentist estimation class object
Comparing plot method plot2compare for three Bayesian estimation class...
Comparing plot method plot2compare for three Bayesian prediction class...
Comparing plot method
Bayesian prediction method for a class object Bayes.fit
Prediction method for the Freq.fit class object
Prediction method
Print of acceptance rates of the MH steps
Description of print
Short summary of the results of class object Bayes.fit
Short summary of the results of class object Freq.fit
Computation Of The Sufficient Statistics
Validation of the chosen model.
Validation of the chosen model.
Validation of the chosen model.
Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function.