Welcome to mixed effects modeling in R

The saemix project is an R package (Comets, Lavenu, and Lavielle 2017) available in CRAN that implements the Stochastic Approximation of the EM (SAEM) algorithm introduced in (Kuhn and Lavielle 2004). This algorithm is state-of-the-art method for fitting, possibly non linear, models in agronomy, animal breeding or Pharmacokinetics-Pharmacodynamics (PKPD) analysis.

Thus far, the main area using the package thus far is Pharmacology, especially to understand how drugs, under development, behave in the body or how the body reacts to a drug during clinical trials (see (Karimi, Lavielle, and Moulines 2020) or (Samson, Lavielle, and Mentré 2006)) but we ought to aim at a more general audience of biostatisticians dealing with nonlinear mixed effects modeling.

saemix is licensed under GPL-2 | GPL-3 [expanded from: GPL (>=2)].

References

Comets, Emmanuelle, Audrey Lavenu, and Marc Lavielle. 2017. “Parameter Estimation in Nonlinear Mixed Effect Models Using Saemix, an R Implementation of the Saem Algorithm.” Journal of Statistical Software, Articles 80 (3): 1–41. doi:10.18637/jss.v080.i03.

Karimi, Belhal, Marc Lavielle, and Eric Moulines. 2020. “F-Saem: A Fast Stochastic Approximation of the Em Algorithm for Nonlinear Mixed Effects Models.” Computational Statistics & Data Analysis 141. Elsevier: 123–38.

Kuhn, Estelle, and Marc Lavielle. 2004. “Coupling a stochastic approximation version of EM with an MCMC procedure.” ESAIM: Probability and Statistics 8. EDP-Sciences: 115–31. http://eudml.org/doc/245020.

Samson, Adeline, Marc Lavielle, and France Mentré. 2006. “Extension of the Saem Algorithm to Left-Censored Data in Nonlinear Mixed-Effects Model: Application to Hiv Dynamics Model.” Computational Statistics & Data Analysis 51 (3). Elsevier: 1562–74.