Regularized Estimation in Mixed Effects Model
AIC for remix object
BIC for remix object
BICc
Compute final estimation
REMixed algorithm over a grid of
eBIC
extract remix results from cvRemix object
Get monolix demo project path
Adaptive Gauss-Hermite approximation of log-likelihood derivatives
Generate individual parameters
Initialization strategy
Model from Clairon and al.,2023
Model from Pasin and al.,2019
Generate trajectory of PK model
Plot of cv.remix object
Calibration plot
Log-likelihood convergence
IC plot
Plot initialization
Display the value of parameters at each iteration
Extract Data for REMixed Algorithm from a Monolix Project
REMixed algorithm
REMixed : Regularisation & Estimation for Mixed effects model
REMixed results
Implementation of an algorithm in two steps to estimate parameters of a model whose latent dynamics are inferred through latent processes, jointly regularized. This package uses 'Monolix' software (<https://monolixsuite.slp-software.com/>), which provide robust statistical method for non-linear mixed effects modeling. 'Monolix' must have been installed prior to use.