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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{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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[](https://cran.r-project.org/web/checks/check_results_nlmixr.html)
[](https://cran.r-project.org/package=nlmixr)
[](https://cran.r-project.org/package=nlmixr)
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***
`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")
```