A comprehensive collection of non-linear mixed effect (nlme) model algorithms:
- First Order Conditional Estimate (
FOCE)
## eg
nlmixr(modelfn, data, "foce");
- First Order Conditional Estimate with interaction (FOCEi),
## eg
nlmixr(modelfn, data, "focei");
- Adaptive Gaussian quadrature (with Laplacian approximation as a special case see
glmm and glmm2),
- Stochastic Approximation Estimation-Maximization (SAEM).
## eg
nlmixr(modelfn, data, "saem")
## eg
nlmixr(modelfn, data, "nlme")
Other features
- A minimalist, intuitive, expressive, and domain-specific nlme modeling language.
- The capability of joint modeling of multiple endpoints.
- A revamped SAEM engine with improved speed and stability.
- The capability of out-of-box Visual Predictive Checks (VPC) after a model fit.
- The capability of out-of-box sophisticated Clinical Trial Simulation (CTS) after a model fit.
- The capability of an out-of-box comprehensive diagnostic kit with a direct hook to xpose after a model fit.
- The capability of modeling “odd type” data, including binary data, count data, and bounded clinical endpoint (e.g., ADAS-cog has a range of 0 to 70).
- Parallel computing ODE solving via the openmp package -- an industry's first among the current population PK/PD simulators to the best of our knowledge.
- An intuitive, powerful, graphic user interface (GUI) based project manager in shinyMixR.
