[NMusers] RE: nlmixr 1.0 released on github

From: Wang, Wenping <wenping.wang_at_novartis.com>
Date: Fri, 24 Aug 2018 19:46:00 +0000

Oh, I forgot to mention nlmixr’s precision is not too shabby in comparison to the industry standard in a recent simulation study.
https://github.com/nlmixrdevelopment/nlmixr/blob/master/inst/comparison%20of%20NONMEM_nlmixr%20Sparse%20samples.pdf

This is not the fairest and most comprehensive comparison out there. The promising results suggest a more thorough study is warranted.

Best,
Wenping Wang

From: owner-nmusers_at_globomaxnm.com<mailto:owner-nmusers_at_globomaxnm.com> <owner-nmusers_at_globomaxnm.com<mailto:owner-nmusers_at_globomaxnm.com>> On Behalf Of Wang, Wenping
Sent: Friday, August 24, 2018 12:29 PM
To: nmusers_at_globomaxnm.com<mailto:nmusers_at_globomaxnm.com>
Subject: [NMusers] nlmixr 1.0 released on github

Today is a bit milestone for nlmixr: nlmixr released its version 1.0 on github (https://github.com/nlmixrdevelopment/nlmixr).

In October 2016, nlmixr announced its birth at the ACoP in Seattle. The assignment of version 0.6 to the initial nlmixr was not completely arbitrary or without consideration. The nlmixr team felt nlmixr then was far from a finished product. Time flies in this short two years, now nlmixr the new kid has finally made the first baby step. We, the nlmixr team, are rather proud of nlmixr’s first step. We believe this release of nlmixr is solid for the following reasons:

- A comprehensive collection of non-linear mixed effect (nlme) model algorithms: First Order Conditional Estimate (FOCE), First Order Conditional Estimate with interaction (FOCEi), Adaptive Gaussian quadrature (with Laplacian approximation as a special case), and Stochastic Approximation Estimation-Maximization (SAEM).

- A minimalist, intuitive, expressive, and domain-specific nlme modeling language.

- The capability of joint modeling of multiple endpoints.

- A revamped SAEM engine with improved speed and stability.

- The capability of out-of-box Visual Predictive Checks (VPC) after a model fit.

- The capability of out-of-box sophisticated Clinical Trial Simulation (CTS) after a model fit.

- The capability of an out-of-box comprehensive diagnostic kit with a direct hook to xpose after a model fit.

- The capability of modeling “odd type” data, including binary data, count data, and bounded clinical endpoint (e.g., ADAS-cog has a range of 0 to 70).

- Parallel computing ODE solving via the openmp package -- an industry's first among the current population PK/PD simulators to the best of our knowledge.

- An intuitive, powerful, graphic user interface (GUI) based project manager in shinyMixR.
The nlmixr team has been overwhelmed and humbled by the heart-felt outpouring of support, enthusiasm and care to this free and open-source tool from the global pharmacometrics community since nlmixr’s arrival. Your constructive criticism and suggestions have made nlmixr so much better than its first public release. nlmixr is still not a finished product. We hope the added functionality and stability would make your modeling experience more enjoyable. We hope you like this nlmixr release and continue to support this community-based nlme tool.


Received on Fri Aug 24 2018 - 15:46:00 EDT

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