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nlmixr

nlmixr: an R package for population PKPD modeling

https://github.com/nlmixrdevelopment/nlmixr

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    3 of 17 committers (17.6%) from academic institutions
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    Institutional organization owner
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  • ○
    Scientific vocabulary similarity
    Low similarity (18.4%) to scientific vocabulary

Keywords from Contributors

rcpp c-plus-plus-11 c-plus-plus-14 c-plus-plus-17 c-plus-plus-20 r-packages
Last synced: 11 months ago · JSON representation

Repository

nlmixr: an R package for population PKPD modeling

Basic Info
Statistics
  • Stars: 112
  • Watchers: 20
  • Forks: 46
  • Open Issues: 67
  • Releases: 35
Created almost 10 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# nlmixr: an R package for population PKPD modeling


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***

![nlmixr](logo.png)


`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. The nlme and SAEM estimation routines can be accessed using a
universal user interface (UUI), that provides universal model and
parameter definition syntax and results in a fit object that can be
used as input into the `Xpose` package. Running nlmixr using the UUI
is described in [this vignette](https://nlmixrdevelopment.github.io/nlmixr/articles/running_nlmixr.html).

Under the hood `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 using the nlme algorithm;
3. `nlme_ode()` for general dynamic models defined by ordinary
   differential equations (ODEs) of population data using the nlme
   algorithm;
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 the
   adaptive Gaussian quadrature algorithm.

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

Documentation can be found at https://nlmixrdevelopment.github.io/nlmixr/, and we maintain a comprehensive and ever-growing guide to using `nlmixr` at our [bookdown site](https://nlmixrdevelopment.github.io/nlmixr_bookdown/index.html).

More examples and the associated data files are available at 
https://github.com/nlmixrdevelopment/nlmixr/tree/master/vignettes.

We recommend you have a look at [`RxODE`](https://nlmixrdevelopment.github.io/RxODE/articles/RxODE-intro.html), the engine upon which `nlmixr` depends, as well as [`xpose.nlmixr`](https://github.com/nlmixrdevelopment/xpose.nlmixr), which provides a link to the seminal nonlinear mixed-effects model diagnostics package [`xpose`](https://uupharmacometrics.github.io/xpose/), and [`shinyMixR`](https://github.com/RichardHooijmaijers/shinyMixR), which provides a means to build a project-centric workflow around nlmixr from the R command line and from a streamlined [`shiny`](https://shiny.rstudio.com/) front-end application. Members of the nlmixr team also contribute to the [`ggPMX`](https://github.com/ggPMXdevelopment/ggPMX), [`xgxr`](https://github.com/Novartis/xgxr) and [`pmxTools`](https://github.com/kestrel99/pmxTools) packages. For PKPD modeling (with ODE and dosing history) with
[Stan](http://mc-stan.org/), check out Yuan Xiong's package [`PMXStan`](https://github.com/yxiong1/pmxstan).

## Installation

When on CRAN, you can install the released version of nlmixr from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("nlmixr")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("nlmixrdevelopment/nlmixr")
```

Owner

  • Name: nlmixr
  • Login: nlmixrdevelopment
  • Kind: organization

GitHub Events

Total
  • Issues event: 5
  • Watch event: 7
  • Issue comment event: 6
  • Fork event: 1
Last Year
  • Issues event: 5
  • Watch event: 7
  • Issue comment event: 6
  • Fork event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 2,219
  • Total Committers: 17
  • Avg Commits per committer: 130.529
  • Development Distribution Score (DDS): 0.496
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Matthew Fidler 5****r 1,119
Fidler m****r@g****m 701
wwang-at-github w****8@g****m 161
Bill Denney w****y@h****m 73
Rik Schoemaker r****r@o****u 42
minalp22 v****3@c****u 42
kiran k****u@g****m 39
kestrel99 j****l@g****m 17
Justin Wilkins j****s@o****m 14
baltcir1 i****a@n****m 2
bgoodri g****n@g****m 2
Johannes Ranke j****e@u****e 2
Andrew Johnson a****n@p****u 1
Andrew Stein a****n@g****m 1
wangwez w****z@l****n 1
mntrame 3****e 1
Lionel Henry l****y@g****m 1
Committer Domains (Top 20 + Academic)
localhost.localdomain: 1 postgrad.curtin.edu.au: 1 uni-bremen.de: 1 novartis.com: 1 occams.com: 1 columbia.edu: 1 occams.eu: 1 humanpredictions.com: 1

Issues and Pull Requests

Last synced: almost 3 years ago

All Time
  • Total issues: 93
  • Total pull requests: 9
  • Average time to close issues: 29 days
  • Average time to close pull requests: about 2 months
  • Total issue authors: 42
  • Total pull request authors: 4
  • Average comments per issue: 4.12
  • Average comments per pull request: 0.78
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 12
  • Pull requests: 1
  • Average time to close issues: 14 days
  • Average time to close pull requests: N/A
  • Issue authors: 9
  • Pull request authors: 1
  • Average comments per issue: 1.5
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • billdenney (25)
  • namtien0312 (6)
  • mattfidler (5)
  • diabloyg (4)
  • dzianismr (4)
  • sliao999 (3)
  • hrishikesh1985 (3)
  • ghost (3)
  • AprilCCC (2)
  • nlmixr-user (2)
  • Generalized (2)
  • Chickenlover0908 (2)
  • jranke (2)
  • WenYao-Mak (2)
  • smouksassi (1)
Pull Request Authors
  • mattfidler (4)
  • andrjohns (2)
  • billdenney (2)
  • eddelbuettel (1)
Top Labels
Issue Labels
enhancement (2)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads: unknown
  • Total docker downloads: 42,053
  • Total dependent packages: 2
    (may contain duplicates)
  • Total dependent repositories: 3
    (may contain duplicates)
  • Total versions: 29
cran.r-project.org: nlmixr

Nonlinear Mixed Effects Models in Population PK/PD

  • Versions: 20
  • Dependent Packages: 2
  • Dependent Repositories: 3
  • Docker Downloads: 42,053
Rankings
Forks count: 1.6%
Stargazers count: 3.8%
Average: 9.3%
Dependent packages count: 14.0%
Dependent repos count: 17.6%
Last synced: almost 2 years ago
conda-forge.org: r-nlmixr
  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Forks count: 24.0%
Stargazers count: 29.7%
Dependent repos count: 34.0%
Average: 34.7%
Dependent packages count: 51.2%
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.0 depends
  • Matrix * imports
  • Rcpp >= 0.12.3 imports
  • RxODE >= 1.1.5 imports
  • backports * imports
  • brew * imports
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  • fastGHQuad * imports
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  • lbfgsb3c * imports
  • magrittr * imports
  • methods * imports
  • minqa * imports
  • n1qn1 >= 6.0.1 imports
  • nlme * imports
  • parallel * imports
  • rex * imports
  • symengine * imports
  • Deriv * suggests
  • Rvmmin * suggests
  • broom.mixed * suggests
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