NMproject

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 31)

Turning R and RStudio into a NONMEM model development environment

NMproject Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# NMproject <a href='https://tsahota.github.io/NMproject/'><img src='man/figures/logo.svg' align="right" height="139" /></a>

[![CRAN
status](https://www.r-pkg.org/badges/version/NMproject)](https://CRAN.R-project.org/package=NMproject)
[![R-CMD-check](https://github.com/tsahota/NMproject/workflows/R-CMD-check/badge.svg)](https://github.com/tsahota/NMproject/actions)
[![Codecov test
coverage](https://codecov.io/gh/tsahota/NMproject/branch/master/graph/badge.svg)](https://app.codecov.io/gh/tsahota/NMproject?branch=master)
[![Lifecycle:
maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)

Script based ‘NONMEM’ model development in RStudio intended for
intermediate to advanced R users.

-   NONMEM code library
-   End-to-end script based model development workflows
-   Scale to groups of runs and complex workflows
-   100% flexibility through tracked manual edits to model files
-   Customisable to multiple infrastructure types

## Prerequisites

-   PsN &gt;= 4.4.8
-   NONMEM installed with valid license
-   RStudio

## Installation

You can install the released version of NMproject from
[CRAN](https://CRAN.R-project.org) with:

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

To install the latest version of NMproject from
[GitHub](https://github.com/):

``` r
if(!require("devtools")) install.packages("devtools")
devtools::install_github("tsahota/NMproject")
```

To install a specific release (e.g. v0.5.1) on
[GitHub](https://github.com/) use the following command:

``` r
devtools::install_github("tsahota/NMproject@v0.5.1")
```

Load the package with

``` r
library(NMproject)
```

## Getting started with NMproject

Two options:

1.  Running the
    [demo](https://tsahota.github.io/NMproject/articles/articles/NMproject.html#demo-1)
    is easiest way to familiarise your with NMproject.
2.  Reading the website
    [vignette](https://tsahota.github.io/NMproject/articles/articles/NMproject.html).

## Code snippets

Use of pipes, `%>%`, make it easy to code sequences of operations to
model objects.

Following snippet adds covariates to model object, `m2`:

-   create a separate child control file
-   add a covariate relationship to it (using PsN SCM syntax)
-   run

``` r
m2WT <- m2 %>% child(run_id = "m2WT") %>%
  add_cov(param = "CL", cov = "WT", state = "power") %>%
  run_nm()
```

Graphical RStudio ‘Addins’ exist for reviewing the changes that
functions like `add_cov()` make before execution and performing
`nm_tran()` checks.

For more complex operations use fully tracked manual edits.

Apply fully customisable diagnostic reports to one or multiple objects
with `nm_render()` like so:

``` r
c(m1, m2) %>% nm_render("Scripts/basic_gof.Rmd")
## Saves html diagnostic reports in "Results" directory
```

The template `Scripts/basic_gof.Rmd` can also be run as an R notebook
for interactively customising to your specific model evaluation
criteria.

Here’s a snippet for producing PPCs and VPCs:

-   create a new (child) control stream
-   updating initial estimates to final estimates
-   convert it to a simulation control file
-   run
-   generate customised PPCs and VPCs from the outputs

``` r
m2s <- m2 %>% child(run_id = "m2s") %>%
  update_parameters(m2) %>%
  convert_to_simulation(subpr = 50) %>%
  run_nm()

m2s %>% nm_render("Scripts/basic_vpc.Rmd")
m2s %>% nm_render("Scripts/basic_ppc.Rmd")
```

Advanced functionality enables groups of runs to be handled with the
same concise syntax (no loops). For example:

-   create 5 child runs
-   Randomly perturb the initial estimates of $THETA and $OMEGA
-   run them all in their own subdirectory for tidiness.

``` r
m1rep <- m1 %>% child(run_id = 1:5) %>% 
  init_theta(init = rnorm(init, mean = init, sd = 0.3)) %>%
  init_omega(init = runif(init, min = init/2, max = init*2)) %>%
  run_in("Models/m1_perturb_inits") %>%
  run_nm()
```

See the website
[vignette](https://tsahota.github.io/NMproject/articles/articles/NMproject.html)
for more examples

NMproject

NMproject Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

NMproject

NMproject Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README