Provides a unified workflow for building, fitting using external engines, and evaluating ordinary differential equation (ODE)-based pharmacokinetic/pharmacodynamic (PK/PD) models. Supports generation of estimation scenarios and control files for external engines (e.g., 'Monolix'), simulation of models using 'rxode2', and creation of goodness-of-fit diagnostics. Includes tools for covariate modeling, virtual population design, and local and global sensitivity analyses.

SimuRg provides a comprehensive workflow for non-linear mixed-effects model development in pharmacometrics. The package provides the entire modeling pipeline: from model calibration with Monolix fitter(2023) and output processing to goodness-of-fit visualization, simulation, and sensitivity analysis. To use Monolix, it should be installed.
Key features:
install.packages("SimuRg")
First of all, the model should be calibrated with Monolix fitter. For this goal, Monolix should be installed on the computer. As this software have commercial license, we start our example with the conversion from the Monolix output files into the generalized fit output.
library("SimuRg")
library(stringr)
# Convert Monolix project results
test_folder <- system.file("extdata", "Monolix_objects", package = "SimuRg")
if (substr(test_folder, nchar(test_folder), nchar(test_folder)) != "/")
test_folder <- str_c(test_folder, "/")
pro_name <- "proj-solo"
result <- sg_converter(folder_path = test_folder, proj_name = pro_name)
# Running goodness-of-fit objects
sg_gof_obpr(result)
sg_gof_res(
fpath_i = result,
res_type = "IWRES",
vs_time = TRUE
)
sg_gof_par_cov(
fpath_i = result,
ptype = "IndParvsCov",
cont_cov = cont_cov,
cat_cov = cat_cov
)
sg_gof_par_dist(fpath_i = result)
sg_gof_res_dist(fpath_i = result, res_type = "IWRES")
sg_gof_res(
fpath_i = result,
res_type = "IWRES",
vs_time = TRUE
)