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Nlmixr Available on Github


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https://github.com/nlmixrdevelopment/nlmixr

The nlmixr team is very proud to announce the release of nlmixr, an open source package for population PK and PKPD modelling developed by Wenping Wang and Yuan Xiong. nlmixr builds on Wenping’s RxODE package for simulation of nonlinear mixed effect models using ordinary differential equations, by implementing parameter estimation algorithms like nlme, gnlmm and SAEM. nlmixr greatly expands the utility of existing packages (like nlme) by providing an efficient and versatile way to specify pharmacometric models and dosing scenarios, with rapid execution due to compilation to C++.

The source is now available at https://github.com/nlmixrdevelopment/nlmixr along with a vignette, and the models and datasets used in the nlmixr/nlme poster to be presented at ACoP (W-66): nlmixr: an open-source package for pharmacometric modelling in R on Wednesday, 26 October, 2016, 7:30-9:30 am. At this point, nlmixr is still very much in a testing stage and is under lively development. Get in touch, see us at ACoP to discuss ways you could contribute (Wenping Wang, Yuan Xiong, Justin Wilkins and Rik Schoemaker will all be there), or try it out for yourself, and experience the joy of a population modelling package within R! A live demo will be possible, so come see Rik if you are interested!

About nlmixr
nlmixr is an R package for fitting general dynamic models, pharmacokinetic (PK) models and pharmacokinetic-pharmacosynamic (PKPD) models in particular, with either individual data or population data. nlmixr has five main modules: 1) dynmodel() and its mcmc cousin dynmodel.mcmc() for nonlinear dynamic models of individual data; 2) nlme_lin_cmpt()for one to three linear compartment models of population data with first order absorption, or i.v. bolus, or i.v. infusion; 3) nlme_ode() for general dynamic models defined by ordinary differential equations (ODEs) of population data; 4) saem_fit for general dynamic models defined by ordinary differential equations (ODEs) of population data by the Stochastic Approximation Expectation-Maximization (SAEM) algorithm; 5) gnlmm for generalized non-linear mixed-models (possibly defined by ordinary differential equations) of population data by adaptive Gaussian quadrature algorithm.

A few utilities to facilitate population model building are also included in nlmixr.

nlmixr development url: https://github.com/nlmixrdevelopment