This is not an exhaustive list, but aims to cover most tools of interest, both commercial and free.

GNU Octave:

  • A free, but less fully-featured alternative to MATLAB.

  • Julia is a high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing.

  • A heavily-optimized platform for performing complex mathematical computing tasks.

  • Easy, fast and powerful tool for parameter estimation in non-linear mixed effect models, model diagnosis and assessment, and advanced graphical representation. Accompanied by supporting products Datxplore, Mlxplore and Simulx.

  • nlmixr is an R package for fitting general dynamic models, pharmacokinetic (PK) models and pharmacokinetic-pharmacodynamic (PKPD) models in particular, with either individual data or population data. It’s free and open source, and will remain so.

  • The gold standard software in Population Pharmacokinetic and Pharmacokinetic-Pharmacodynamic modelling.

Perl-speaks-NONMEM (PsN)

  • Perl-speaks-NONMEM (PsN) is a collection of Perl modules and programs aiding in the development of non-linear mixed effect models using NONMEM. The functionality ranges from solutions to simpler tasks such as parameter estimate extraction from output files, data file subsetting and resampling, to advanced computer-intensive statistical methods. PsN includes stand-alone tools for the end-user as well as development libraries for method developers.


  • Phoenix NLME software is a population modeling and simulation solution for scientists with all levels of experience—from the most advanced modelers to novice PK/PD scientists. This comprehensive package includes integrated data preparation, modeling, and graphics tools, and uses the same GUI as Phoenix WinNonlin.

  • Pirana offers a powerful workbench for pharmacometric modeling, on your desktop or in the cloud.

  • PMXStan is an R package aiming at facilitating Stan-based modeling building, fitting, diagnosis, and simulations for pharmacometrics uses.


  • Pumas is a one-stop integrated modelling and simulation platform powered by the Julia language. It can perform non-compartmental analysis (NCA), nonlinear mixed-effects modelling (NLME), Bioequivalence (BE), in vitro-in vivo correlation (IVIVC) and clinical trial simulations (CTS). Further, Pumas provides convenient way of handling of multi-scale PBPK, QSP models. DeepNLME is the state-of-the-art product of Pumas that seamlessly integrates NLME and Deep Learning, via scientific machine learning. Pumas comes with a full suite of productivity tools to pre- and post-process your analyses in an interactive manner. Scientists can easily convert complex models to dashboards effortlessly to collaborate with interdisciplinary teams. Pumas was developed as a cloud-first technology that allows scientists scale to thousands of CPUs and GPUs with a single click. The software is free for non-commercial research and training..

  • R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. 

  • Stan provides a full Bayesian platform for statistical modeling, data analysis, and prediction. Users specify log density functions in Stan’s probabilistic programming language and get full Bayesian statistical inference with MCMC sampling (NUTS, HMC), approximate Bayesian inference with variational inference (ADVI), penalized maximum likelihood estimation with optimization (L-BFGS), amongst other features. Stan’s math library provides differentiable probability functions & linear algebra (C++ autodiff). Additional R packages provide expression-based linear modeling, posterior visualization, and leave-one-out cross-validation.

  • xpose was designed as a ggplot2-based update to xpose4. xpose aims to reduce the post processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.