NMproject

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 >= 4.4.8
• NONMEM installed with valid license
• RStudio

Installation

You can install the released version of NMproject from
CRAN with:

install.packages("NMproject")

To install the latest version of NMproject from
GitHub:

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

To install a specific release (e.g. v0.5.1) on
GitHub use the following command:

devtools::install_github("tsahota/[email protected]")

Load the package with

library(NMproject)

Getting started with NMproject

Two options:

1. Running the
   demo
   is easiest way to familiarise your with NMproject.
2. Reading the website
   vignette.

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

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:

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

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.

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
for more examples