PSM: Non-Linear Mixed-Effects modelling using Stochastic Differential Equations. This package provides functions for estimation of linear and non-linear mixed-effects models using stochastic differential equations. Moreover it provides functions for finding smoothed estimates of model states and for simulation. The package allows for any multivariate non-linear time-variant model to be specified, and it also handles multidimensional input, co-variates, missing observations and specification of dosage regimen.

References in zbMATH (referenced in 9 articles )

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  1. Jacob Leander, Joachim Almquist, Anna Johnning, Julia Larsson, Mats Jirstrand: NLMEModeling: A Wolfram Mathematica Package for Nonlinear Mixed Effects Modeling of Dynamical Systems (2020) arXiv
  2. Mena, Hermann; Pfurtscheller, Lena-Maria; Romero-Leiton, Jhoana P.: Random perturbations in a mathematical model of bacterial resistance: analysis and optimal control (2020)
  3. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  4. Delattre, Maud; Lavielle, Marc: Coupling the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models (2013)
  5. Madsen, Henrik; Thyregod, Poul: Introduction to general and generalized linear models. (2011)
  6. Picchini, Umberto; Ditlevsen, Susanne: Practical estimation of high dimensional stochastic differential mixed-effects models (2011)
  7. Philipsen, K. R.; Christiansen, L. E.; Hasman, H.; Madsen, H.: Modelling conjugation with stochastic differential equations (2010)
  8. Klim, Søren; Mortensen, Stig Bousgaard; Kristensen, Niels Rode; Overgaard, Rune Viig; Madsen, Henrik: Population stochastic modelling (PSM) - an R package for mixed-effects models based on stochastic differential equations (2009) ioport
  9. Strathe, A. B.; Sørensen, H.; Danfær, A.: A new mathematical model for combining growth and energy intake in animals: the case of the growing pig (2009)