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Simulx

Period: 
Jun 09 2014

Simulx is an R function for easily computing predictions and simulating data from both Mlxtran and PharmML models.
 

Simulx is based on MlxCompute, the model simulation engine developed by Lixoft. MlxCompute combines the Mlxtran language interpreter with the equation solvers to compute efficiently complex systems of ordinary differential equations (ODEs) and delayed differential equations (DDEs).

Simulx  takes advantage of the modularity of hierarchical models for simulating different components of a model: models for population parameters, individual covariates, individual parameters and longitudinal data, including continuous, count, categorical, and time-to-event data. simulx is also extremely flexible for defining complex dose regimens. Then, simulx will be the core of the next version of the DDMoRe Clinical Trial Simulator.
 

Several examples of simulx using PharmML are already available. This is made possible thanks to a first PharmML -> MLXtran translator developed by Lixoft for DDMoRe.

Simulx was developed  by Inria and is governed by the open-source CeCILL-B license (http://www.cecill.info/index.en.html).

MlxCompute is developed by Lixoft; it is available free of charge for students, academics and regulation agencies, and is commercial for industry users.

Public release

This first public release of simulx is a Windows and Linux version.  MacOS version is to come soon.

This release includes an extensive documentation covering the different features of simulx and illustrated with examples. All the R codes and the models used for the documentation are available with simulx in the folder demos.

Tutorials

Documentation

Installation Procedure

  1. install Mlxlibrary from the Lixoft website
  2. download (see downloads) and unzip or untar
  • MlxR110.zip (R version for Windows)
  • MlxR110.tgz   (R version for Linux)
  • Mlxm110.zip  (MATLAB version for Windows)
  • Mlxm110.tgz  (MATLAB version for Linux)

Running Mlxm:

  1. start Matlab
  2. change the working directory to Mlxm110
  3. change the working directory to Mlxm110

>>initMlxm

You can then run any demo or create your own MATLAB script.

Running MlxR:

  1. start R
  2. change the working directory to MlxR110
  3. source initMlxR.R

>source('initMlxR.R')

You can then run any demo or create your own R script.

Work package: 

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