lilyycl / pharmr

R wrapper to pharmpy

pharmr is an R package and toolkit for pharmacometrics. It consists of

• A model abstraction as a foundation for higher level operations
• Functions for
  • manipulation of models
  • extraction of model estimation results
  • manipulation of datasets
  • various calculations and evaluations
  • running models
  • running higher level workflows or tools

pharmr is implemented as an R wrapper to Pharmpy. It provides an R interface to all functions found in the modeling and tools modules (documented here). Each function is also available via the help-function (or ?).

Installation

Install from github

pharmr uses the package reticulate for calling Python from R. When installing pharmr, reticulate will give a prompt to set up the reticulate environment. In order to use pharmr, you need to have Python 3.9, 3.10 or 3.11.

To install pharmr and Pharmpy:

remotes::install_github("pharmpy/pharmr", ref="main")
pharmr::install_pharmpy()

Sometimes it is necessary to specify which version of Python to use. To make reticulate use Python 3.9 in its environment, run the following:

Sys.setenv(RETICULATE_MINICONDA_PYTHON_VERSION="3.9")

Install from CRAN

pharmr is available on CRAN and can be installed with

install.packages("pharmr")
pharmr::install_pharmpy()

Note that pharmr and Pharmpy are under rapid development and the version on github will be a much more up to date version than the version on CRAN. Check the badges above to see the version numbers for CRAN and github.

Using pharmr

>>> library(pharmr)
>>> res <- read_modelfit_results("run1.mod")
>>> res$parameter_estimates
  THETA(1)   THETA(2)   THETA(3) OMEGA(1,1) OMEGA(2,2) SIGMA(1,1)
0.00469555 0.98425800 0.15892000 0.02935080 0.02790600 0.01324100
>>> model <- read_model("run1.mod")
>>> model$parameters
       name     value  lower    upper    fix
   THETA(1)  0.004693   0.00  1000000  False
   THETA(2)  1.009160   0.00  1000000  False
   THETA(3)  0.100000  -0.99  1000000  False
 OMEGA(1,1)  0.030963   0.00       oo  False
 OMEGA(2,2)  0.031128   0.00       oo  False
 SIGMA(1,1)  0.013086   0.00       oo  False
>>>

A simple example of reading a model, performing a simple transformation, and running the model in NONMEM:

model <- load_example_model('pheno') %>%
  add_peripheral_compartment() %>%
  fit()