## saemix: Stochastic Approximation Expectation Maximization (SAEM)
algorithm

The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group (http://group.monolix.org/).

Version: |
1.2 |

Imports: |
graphics, stats, methods |

Published: |
2014-02-25 |

Author: |
Emmanuelle Comets, Audrey Lavenu, Marc Lavielle. |

Maintainer: |
Emmanuelle Comets <emmanuelle.comets at inserm.fr> |

License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |

NeedsCompilation: |
no |

Citation: |
NA |

Materials: |
NA |

CRAN checks: |
saemix results |

#### Downloads: