RDarwin_NLME_RPostprocessing.Rmd
library(Certara.RDarwin)This vignette provides an illustrative workflow for using the
Certara.RDarwin package, a toolkit designed to facilitate
the usage of pyDarwin
with the Certara NLME pharmacometric modeling engine from the R command
line. This particular example demonstrates the step-by-step process of
creating, configuring, and executing a model search using RDarwin in
combination with the pyDarwin framework.
The data set used in this example is pkOralBolus.csv and
should be downloaded to your working directory. Click here
to download.
The first section of the workflow involves setting up the model. In this example, we will configure a first-order absorption model with parameterization set to “Clearance.” We specify several model characteristics, such as the number of compartments, absorption type, and parameterization. Additionally, we introduce parameters to the model and make modifications to their initial estimates. In this workflow, the following actions are taken:
get_PMLParametersSets function to configure the model’s
structures, including the number of compartments, absorption type, and
parameterization.
modelPMLCodes <-
get_PMLParametersSets(
CompartmentsNumber = c(1, 2),
Absorption = c("First-Order"),
Parameterization = "Clearance"
)add_StParm
function.
modelPMLCodes <-
add_StParm(
modelPMLCodes,
StParmName = "F",
Type = "LogitNormal",
State = "Searched",
ThetaStParm = Theta(Name = "tvlogitF", InitialEstimates = -1),
OmegaStParm = Omega(Name = "nlogitF", State = "Searched"),
DosepointArgName = "bioavail"
)modify_Theta and modify_Omega
functions, respectively.
# list Thetas available
list_Thetas(modelPMLCodes)
#> [1] "tvCl" "tvV" "tvKa" "tvlogitF" "tvCl2" "tvV2"
# list Omegas available
list_Omegas(modelPMLCodes)
#> [1] "nCl" "nV" "nKa" "nlogitF" "nCl2" "nV2"
# change initial estimates for Theta and Omega
modelPMLCodes <-
modelPMLCodes |>
modify_Theta(
Name = "tvV",
InitialEstimates = c(0, 5, 1000)
) |>
modify_Omega(
Name = "nCl",
InitialOmega = 5
)modify_Observation function.
# list observations available
list_Observations(modelPMLCodes)
#> [1] "CObs"
modelPMLCodes <-
modelPMLCodes |>
modify_Observation(
ObservationName = "CObs",
SigmasChosen =
Sigmas(
Proportional = 0,
AdditiveMultiplicative = list(PropPart = 0.1, AddPart = 10)
)
)
# add zero order absorption to one compartment model
modelPMLCodes <-
modelPMLCodes |>
add_StParm(
StParmName = "Rate",
Type = "LogNormal2",
State = "Searched",
ThetaStParm = Theta(Name = "tvlogRate", InitialEstimates = 0),
OmegaStParm = Omega(Name = "nlogRate", State = "Present"),
DosepointArgName = "rate",
PMLStructures = c("PK1FOC")
)
# see the resulted template
print(modelPMLCodes)
#> PK1FOC
#> test() {
#> cfMicro(A1, Cl / V, first = (Aa = Ka))
#> C = A1 / V
#> dosepoint(Aa{_F[1]}{_Rate[1]}, idosevar = AaDose, infdosevar = AaInfDose, infratevar = AaInfRate)
#> {_F[2]}
#> {_Rate[2]}
#> error(CEps = 10)
#> observe(CObs = C + CEps*sqrt(1 + C*C*(CMultStdev/sigma())^2))
#> stparm(CMultStdev = tvCMultStdev )
#> fixef(tvCMultStdev= c(, 0.1, ))
#>
#>
#> stparm(Cl = tvCl * exp( nCl ))
#> fixef(tvCl= c(, 1, ))
#> ranef(diag(nCl) = c(5))
#> stparm(V = tvV * exp( nV ))
#> fixef(tvV= c(0, 5, 1000))
#> ranef(diag(nV) = c(1))
#> stparm(Ka = tvKa * exp( nKa ))
#> fixef(tvKa= c(, 1, ))
#> ranef(diag(nKa) = c(1))
#>
#> }
#> PK2FOC
#> test() {
#> cfMicro(A1, Cl / V, Cl2 / V, Cl2 / V2, first = (Aa = Ka))
#> C = A1 / V
#> dosepoint(Aa{_F[1]}, idosevar = AaDose, infdosevar = AaInfDose, infratevar = AaInfRate)
#> {_F[2]}
#> error(CEps = 10)
#> observe(CObs = C + CEps*sqrt(1 + C*C*(CMultStdev/sigma())^2))
#> stparm(CMultStdev = tvCMultStdev )
#> fixef(tvCMultStdev= c(, 0.1, ))
#>
#>
#> stparm(Cl = tvCl * exp( nCl ))
#> fixef(tvCl= c(, 1, ))
#> ranef(diag(nCl) = c(5))
#> stparm(V = tvV * exp( nV ))
#> fixef(tvV= c(0, 5, 1000))
#> ranef(diag(nV) = c(1))
#> stparm(Cl2 = tvCl2 * exp( nCl2 ))
#> fixef(tvCl2= c(, 1, ))
#> ranef(diag(nCl2) = c(1))
#> stparm(V2 = tvV2 * exp( nV2 ))
#> fixef(tvV2= c(, 1, ))
#> ranef(diag(nV2) = c(1))
#> stparm(Ka = tvKa * exp( nKa ))
#> fixef(tvKa= c(, 1, ))
#> ranef(diag(nKa) = c(1))
#>
#> }The next step in our workflow involves setting up the engine and simulation parameters. In this example, we use the FOCE-ELS (First-Order Conditional Estimation with Interaction) method for optimization and specify simulation settings. These are the actions performed:
specify_EngineParams function to set up the engine
parameters. In this case, we choose the FOCE-ELS method, configure
standard error estimation, and set the number of iterations for
MAP-Nonparametric optimization.
engine_Setup <- specify_EngineParams(
method = "FOCE-ELS",
stdErr = "None",
numIterMAPNP = 3
)specify_SimParams function. In this
example, we configure the number of replicates and set a seed for
reproducibility.
Sim_Setup <- specify_SimParams(
numReplicates = 10,
seed = 12345678
)Table() function. In this example, we introduce
two output tables. The first table, “ObsData.csv” is generated after
estimation step to get observations (CObs) from the source file. The
second table, “SimData.csv”, is generated during simulation (by setting
the argument ForSimulation = TRUE) and includes simulated
data for CObs.Once the model and parameter setups are complete, we use the
write_ModelTemplateTokens function to generate the template
(template.txt) and token (tokens.json) files,
which define the model structure, its characteristics, and data mappings
- all of which are essential to the model search. This function also
allows the specification of omega
structures to be searched through the argument
OmegaSearchBlock.
# Generate template and token files
ModelTemplateTokens <-
write_ModelTemplateTokens(
TemplateFilePath = file.path(tempdir(), "template.txt"),
TokensFilePath = file.path(tempdir(), "tokens.json"),
Description = "search--123F-12Tlag-1Rate",
Author = "Certara",
DataFilePath = "pkOralBolus.csv",
DataMapping = c(
id = "ID",
time = "time",
AMT = "Dose",
CObs = "CObs"
),
PMLParametersSets = modelPMLCodes,
EstArgs = engine_Setup,
SimArgs = Sim_Setup,
Tables = list(simple_table01, Sim_table01)
)
#> information stored in C:\Users\jcraig\AppData\Local\Temp\RtmpSOj9ME/template.txt and C:\Users\jcraig\AppData\Local\Temp\RtmpSOj9ME/tokens.jsonThe next step in the workflow is the generation of the options file,
options.json. This file is crucial for configuring the
pyDarwin project according to specific requirements. The following
actions are taken:
create_pyDarwinOptions
function. This includes specifying the algorithm, timeouts, directories,
and post-processing settings. In this example, we use post run R code,
which is specified through the postprocess argument.It is assumed to have NLME Engine installed/unzipped and
INSTALLDIR environment variable pointed to the installation
directory (C:/Program Files/Certara/NLME_Engine on
Windows). Please modify it accordingly.
# relative paths are used for convenience
data_dir <- "../../../.."
working_dir <- "{project_dir}/Results"
output_dir <- "{project_dir}/Results/output"
temp_dir <- "{project_dir}/Results/temp"
# move files into temporary folder
file.copy("penaltyDiffObservedSimulatedCmax.R", tempdir())
file.copy("pkOralBolus.csv", tempdir())
optionSetup <- create_pyDarwinOptions(
algorithm = "GA",
model_run_timeout = 2400,
data_dir = data_dir,
working_dir = working_dir,
output_dir = output_dir,
temp_dir = temp_dir,
nlme_dir = Sys.getenv("INSTALLDIR"),
remove_temp_dir = FALSE,
postprocess =
pyDarwinOptionsPostprocess(
use_r = TRUE,
post_run_r_code = "{project_dir}/penaltyDiffObservedSimulatedCmax.R"
)
)write_pyDarwinOptions function to generate the
options.json file based on the setup created in the
previous step.With the template, token, and options files in place, we are ready to
execute the pyDarwin model search. In this final step, we run the search
using the run_pyDarwin function. The following actions are
performed:
pythonPath <- "./venv/Scripts/python.exe"run_pyDarwin function. This function monitors the
search progress and, depending on the configuration, either waits for
the search to complete or exits immediately.
job <- run_pyDarwin(InterpreterPath = pythonPath,
DirectoryPath = tempdir(),
TemplatePath = "template.txt",
TokensPath = "tokens.json",
OptionsPath = "options.json",
Wait = TRUE) Running pyDarwin v2.0.0
Options file found at /tmp/RtmplOWzal/options.json
Preparing project working folder...
Preparing project output folder...
Preparing project temp folder...
Using darwin.MemoryModelCache
Writing intermediate output to /tmp/RtmplOWzal/Results/output/results.csv
Models will be saved in /tmp/RtmplOWzal/Results/models.json
Template file found at /tmp/RtmplOWzal/template.txt
Tokens file found at /tmp/RtmplOWzal/tokens.json
Algorithm: GA
Engine: NLME
random_seed: 11
Project dir: /tmp/RtmplOWzal
Data dir: ../../../..
Project working dir: /tmp/RtmplOWzal/Results
Project temp dir: /tmp/RtmplOWzal/Results/temp
Project output dir: /tmp/RtmplOWzal/Results/output
Key models dir: /tmp/RtmplOWzal/Results/key_models
Search space size: 16
Estimated number of models to run: 264
NLME found: /home/mtomashe/Desktop/installdir/
GCC found: /usr
Search start time: Wed Nov 1 15:59:04 2023
Starting generation 1
RScript found at /usr/lib/R/bin/Rscript
Post Run R code found at /tmp/RtmplOWzal/penaltyDiffObservedSimulatedCmax.R
Not using Post Run Python code
Checking files in /tmp/RtmplOWzal/Results/temp/1/1
Generation = 1, Model 2, Done, fitness = 7570.350, message = No important warnings
Generation = 1, Model 1, Done, fitness = 7615.266, message = No important warnings
Generation = 1, Model 3, Done, fitness = 7615.266, message = No important warnings
Generation = 1, Model 4, Done, fitness = 7406.655, message = No important warnings
Current generation best genome = [0, 1, 1, 0], best fitness = 7406.6551
Best overall fitness = 7406.655099, iteration 1, model 4
No change in fitness for 1 generations, best fitness = 7406.6551
Starting generation 2
Time elapsed: 1.4 min.
Estimated time remaining: 89 min.
Generation = 2, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 2, Model 2, Cache(1-2), fitness = , message = From NLME_1_2: No important warnings
Generation = 2, Model 3, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 2, Model 4, Cache(1-1), fitness = , message = From NLME_1_1: No important warnings
Starting downhill generation = 2 at Wed Nov 1 16:00:27 2023
current best model(s) =
generation 2, ind 1, fitness = 7406.655099211558
generation 2, ind 3, fitness = 7406.655099211558
code for niche (minimal binary) 1 = [0, 1, 1, 0], fitness = 7406.655099211558
Starting downhill step 1, total of 4 in 1 niches to be run.
Time elapsed: 1.4 min.
Estimated time remaining: 86 min.
Generation = 2D01, Model 1, Cache(1-1), fitness = , message = From NLME_1_1: No important warnings
Generation = 2D01, Model 3, Cache(1-2), fitness = , message = From NLME_1_2: No important warnings
Generation = 2D01, Model 2, Done, fitness = 8705.072, message = No important warnings
Generation = 2D01, Model 4, Done, fitness = 8843.169, message = No important warnings
Begin local exhaustive 2-bit search, generation = 2, step = 2
Model for local exhaustive search = 2, phenotype = ([('PML', 0), ('_F', 1), ('_Rate', 1), ('_nlogitF', 0)]) model Num = 1, fitness = 7406.655099211558
4 models in local exhaustive search, 1 bits
10 models in local exhaustive search, 2 bits
Time elapsed: 6.2 min.
Estimated time remaining: 3 h. 43 min.
Generation = 2S01, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 2S01, Model 3, Cache(1-3), fitness = , message = From NLME_1_3: No important warnings
Generation = 2S01, Model 5, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 2S01, Model 6, Done, fitness = 7549.176, message = No important warnings
Generation = 2S01, Model 8, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 2S01, Model 2, Done, fitness = 7562.779, message = No important warnings
Generation = 2S01, Model 10, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 2S01, Model 9, Done, fitness = 7564.889, message = No important warnings
Generation = 2S01, Model 4, Done, fitness = 7538.376, message = No important warnings
Generation = 2S01, Model 7, Done, fitness = 8705.072, message = No important warnings
Done with downhill step, 2. best fitness = 7406.655099211558
Current generation best genome = [0, 1, 1, 0], best fitness = 7406.6551
Best overall fitness = 7406.655099, iteration 1, model 4
No change in fitness for 2 generations, best fitness = 7406.6551
Starting generation 3
Time elapsed: 9.4 min.
Estimated time remaining: 2 h. 48 min.
Generation = 3, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 3, Model 2, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 3, Model 3, Cache(1-1), fitness = , message = From NLME_1_1: No important warnings
Generation = 3, Model 4, Clone(3), fitness = , message = From NLME_1_1: No important warnings
Current generation best genome = [0, 1, 1, 0], best fitness = 7406.6551
Best overall fitness = 7406.655099, iteration 1, model 4
No change in fitness for 3 generations, best fitness = 7406.6551
Starting generation 4
Time elapsed: 9.4 min.
Estimated time remaining: 2 h. 44 min.
Generation = 4, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 4, Model 2, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 4, Model 3, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 4, Model 4, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Starting downhill generation = 4 at Wed Nov 1 16:08:30 2023
current best model(s) =
generation 4, ind 1, fitness = 7406.655099211558
generation 4, ind 2, fitness = 7406.655099211558
code for niche (minimal binary) 1 = [0, 1, 1, 0], fitness = 7406.655099211558
Starting downhill step 1, total of 4 in 1 niches to be run.
Time elapsed: 9.4 min.
Estimated time remaining: 2 h. 37 min.
Generation = 4D01, Model 1, Cache(1-1), fitness = , message = From NLME_1_1: No important warnings
Generation = 4D01, Model 2, Cache(2D01-2), fitness = , message = From NLME_2D01_2: No important warnings
Generation = 4D01, Model 3, Cache(1-2), fitness = , message = From NLME_1_2: No important warnings
Generation = 4D01, Model 4, Cache(2D01-4), fitness = , message = From NLME_2D01_4: No important warnings
Begin local exhaustive 2-bit search, generation = 4, step = 2
Model for local exhaustive search = 4, phenotype = ([('PML', 0), ('_F', 1), ('_Rate', 1), ('_nlogitF', 0)]) model Num = 1, fitness = 7406.655099211558
4 models in local exhaustive search, 1 bits
10 models in local exhaustive search, 2 bits
Time elapsed: 9.4 min.
Estimated time remaining: 2 h. 06 min.
Generation = 4S01, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 4S01, Model 2, Cache(2S01-2), fitness = , message = From NLME_2S01_02: No important warnings
Generation = 4S01, Model 3, Cache(1-3), fitness = , message = From NLME_1_3: No important warnings
Generation = 4S01, Model 4, Cache(2S01-4), fitness = , message = From NLME_2S01_04: No important warnings
Generation = 4S01, Model 5, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 4S01, Model 6, Cache(2S01-6), fitness = , message = From NLME_2S01_06: No important warnings
Generation = 4S01, Model 7, Cache(2S01-7), fitness = , message = From NLME_2S01_07: No important warnings
Generation = 4S01, Model 8, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 4S01, Model 9, Cache(2S01-9), fitness = , message = From NLME_2S01_09: No important warnings
Generation = 4S01, Model 10, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Done with downhill step, 4. best fitness = 7406.655099211558
Current generation best genome = [0, 1, 1, 0], best fitness = 7406.6551
Best overall fitness = 7406.655099, iteration 1, model 4
No change in fitness for 4 generations, best fitness = 7406.6551
Starting generation 5
Time elapsed: 9.4 min.
Estimated time remaining: 109 min.
Generation = 5, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 5, Model 2, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 5, Model 3, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 5, Model 4, Cache(2S01-4), fitness = , message = From NLME_2S01_04: No important warnings
Current generation best genome = [0, 1, 1, 0], best fitness = 7406.6551
Best overall fitness = 7406.655099, iteration 1, model 4
No change in fitness for 5 generations, best fitness = 7406.6551
Starting generation 6
Time elapsed: 9.4 min.
Estimated time remaining: 106 min.
Generation = 6, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 6, Model 2, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 6, Model 3, Cache(2S01-9), fitness = , message = From NLME_2S01_09: No important warnings
Generation = 6, Model 4, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Starting downhill generation = 6 at Wed Nov 1 16:08:30 2023
current best model(s) =
generation 6, ind 1, fitness = 7406.655099211558
generation 6, ind 2, fitness = 7406.655099211558
code for niche (minimal binary) 1 = [0, 1, 1, 0], fitness = 7406.655099211558
Starting downhill step 1, total of 4 in 1 niches to be run.
Time elapsed: 9.4 min.
Estimated time remaining: 99 min.
Generation = 6D01, Model 1, Cache(1-1), fitness = , message = From NLME_1_1: No important warnings
Generation = 6D01, Model 2, Cache(2D01-2), fitness = , message = From NLME_2D01_2: No important warnings
Generation = 6D01, Model 3, Cache(1-2), fitness = , message = From NLME_1_2: No important warnings
Generation = 6D01, Model 4, Cache(2D01-4), fitness = , message = From NLME_2D01_4: No important warnings
Begin local exhaustive 2-bit search, generation = 6, step = 2
Model for local exhaustive search = 6, phenotype = ([('PML', 0), ('_F', 1), ('_Rate', 1), ('_nlogitF', 0)]) model Num = 1, fitness = 7406.655099211558
4 models in local exhaustive search, 1 bits
10 models in local exhaustive search, 2 bits
Time elapsed: 9.4 min.
Estimated time remaining: 68 min.
Generation = 6S01, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = 6S01, Model 2, Cache(2S01-2), fitness = , message = From NLME_2S01_02: No important warnings
Generation = 6S01, Model 3, Cache(1-3), fitness = , message = From NLME_1_3: No important warnings
Generation = 6S01, Model 4, Cache(2S01-4), fitness = , message = From NLME_2S01_04: No important warnings
Generation = 6S01, Model 5, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 6S01, Model 6, Cache(2S01-6), fitness = , message = From NLME_2S01_06: No important warnings
Generation = 6S01, Model 7, Cache(2S01-7), fitness = , message = From NLME_2S01_07: No important warnings
Generation = 6S01, Model 8, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = 6S01, Model 9, Cache(2S01-9), fitness = , message = From NLME_2S01_09: No important warnings
Generation = 6S01, Model 10, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Done with downhill step, 6. best fitness = 7406.655099211558
Current generation best genome = [0, 1, 1, 0], best fitness = 7406.6551
Best overall fitness = 7406.655099, iteration 1, model 4
No change in fitness for 6 generations, best fitness = 7406.6551
-- End of GA component at Wed Nov 1 16:08:30 2023 --
0] Best individual GA is [0, 1, 1, 0] with fitness of 7406.655099
code for niche (minimal binary) 1 = [0, 1, 1, 0], fitness = 7406.655099211558
Starting downhill step 1, total of 4 in 1 niches to be run.
Time elapsed: 9.4 min.
Estimated time remaining: 48 min.
Generation = FND01, Model 1, Cache(1-1), fitness = , message = From NLME_1_1: No important warnings
Generation = FND01, Model 2, Cache(2D01-2), fitness = , message = From NLME_2D01_2: No important warnings
Generation = FND01, Model 3, Cache(1-2), fitness = , message = From NLME_1_2: No important warnings
Generation = FND01, Model 4, Cache(2D01-4), fitness = , message = From NLME_2D01_4: No important warnings
Begin local exhaustive 2-bit search, generation = FN, step = 2
Model for local exhaustive search = 6, phenotype = ([('PML', 0), ('_F', 1), ('_Rate', 1), ('_nlogitF', 0)]) model Num = 1, fitness = 7406.655099211558
4 models in local exhaustive search, 1 bits
10 models in local exhaustive search, 2 bits
Time elapsed: 9.4 min.
Estimated time remaining: 17 min.
Generation = FNS01, Model 1, Cache(1-4), fitness = , message = From NLME_1_4: No important warnings
Generation = FNS01, Model 2, Cache(2S01-2), fitness = , message = From NLME_2S01_02: No important warnings
Generation = FNS01, Model 3, Cache(1-3), fitness = , message = From NLME_1_3: No important warnings
Generation = FNS01, Model 4, Cache(2S01-4), fitness = , message = From NLME_2S01_04: No important warnings
Generation = FNS01, Model 5, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = FNS01, Model 6, Cache(2S01-6), fitness = , message = From NLME_2S01_06: No important warnings
Generation = FNS01, Model 7, Cache(2S01-7), fitness = , message = From NLME_2S01_07: No important warnings
Generation = FNS01, Model 8, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Generation = FNS01, Model 9, Cache(2S01-9), fitness = , message = From NLME_2S01_09: No important warnings
Generation = FNS01, Model 10, Clone(1), fitness = , message = From NLME_1_4: No important warnings
Done with final downhill step. best fitness = 7406.655099211558
Final output from best model is in /tmp/RtmplOWzal/Results/output/FinalResultFile.txt
Number of considered models: 80
Number of models that were run during the search: 11
Number of unique models to best model: 4
Time to best model: 1.4 minutes
Best overall fitness: 7406.655099, iteration 1, model 4
Elapsed time: 9.4 minutes
Search end time: Wed Nov 1 16:08:30 2023
Key models:
Generation = 1, Model 4, Done, fitness = 7406.655, message = No important warningsThe results will be available as a list with elements including
results (a data frame with results of all runs),
FinalResultFile (textual file with the properties of final
model chosen) and FinalControlFile with the final metamodel
as a result of the search.
This vignette demonstrates a complete workflow for using
Certara.RDarwin to configure and execute a model search
with the pyDarwin framework. It highlights the importance of setting up
the model, configuring parameters, and creating the necessary files for
a successful model selection process. By following these steps, users
can effectively perform automated model selection using RDarwin and
pyDarwin.