NMproject

KO | EN

gitlite — search

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

★ 31 Open GitHub ↗

Turning R and RStudio into a NONMEM model development environment

다운로드

최종 버전 다운로드 (.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

다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

NMproject

다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README