Package index • Certara.RsNLME

Authentication and Licensing

Manage your NLME license with these functions.

Use these functions to define various types of PK/PD models.

Use these functions to add/update model parameters.

addCovariate(): Add covariate to model object
removeCovariate(): Remove covariate from structural parameters in a model object.
fixedEffect(): Specifies the initial values, lower bounds, upper bounds, and units for fixed effects in a model
randomEffect(): Sets or updates the covariance matrix of random effects
structuralParameter(): Set structural parameter in model object
addSecondary(): Adds a secondary parameter to model definition
initFixedEffects() `initFixedEffects<-`(): Display/Set initial estimates for fixed effects
update_Thetas(): Update fixed-effect (theta) initial values, bounds, freeze, and enable
update_Omegas(): Update omega (random-effect) initial values and freeze status
update_Sigmas(): Update residual-error (sigma) initial values and freeze status

Use these functions to create and update a textual model object.

Use these functions to edit and copy a model object.

Use these functions to associate model variables with input data columns and to add extra mapping information to the column definition file.

dataMapping(): Initialize input data for PK/PD model
colMapping(): Add column mappings
addExtraDef(): Adds user defined extra column/table definitions to column definition file
addADDL(): Adds ADDL extra column definition to model object
addSteadyState(): Adds Steady State extra column definition to model object
addDoseCycle(): Adds a dosing cycle to model
addMDV(): Adds MDV extra column definition to model object
addReset(): Adds reset instructions to the model

Read, write, and run metamodel (.mmdl) files.

read_mmdl(): Read a metamodel file (.mmdl)
write_mmdl(): Write a metamodel file (.mmdl)
run_metamodel(): Fit the NLME metamodel
extract_mmdl(): extract files used for powershell script
saveUpdatedMetamodel(): workhorse for change_ThetasMmdlin RsNLME.ModelBuilder
create_model_from_metamodel(): Use to create model object from parsed metamodel
create_metamodel_from_model(): Create a metamodel file from an NlmePmlModel

Prepare input data prior to model construction.

imputeMissingCovariates(): Impute missing covariate values
imputeMode(): Modal value with deterministic tie-break (default categorical aggregator)

Use these functions to perform various types of model execution.

fitmodel(): Executes an NLME simple estimation
sortfit(): Executes an NLME simple estimation with sort keys and given scenarios
stepwiseSearch(): Executes an NLME stepwise covariate search
shotgunSearch(): Executes an NLME shotgun covariate search
bootstrap(): Executes an NLME Bootstrap
vpcmodel(): Perform visual predictive check for NLME models
simmodel(): Executes an NLME simulation
profilePertubate(): Executes an NLME profile perturbation
cancelJob(): Generic function for cancelling a job
collectJob(): Collect the typed result of a finished NLME job
NlmeJobStatus(): Reads progress file and returns the status of a job
engineParams(): Main function to specify engine parameters
hostParams(): Create an NLME Parallel Host Configuration
checkHostParams(): Check Host Parameters
tableParams(): Wrapper around NlmeTableDef/NlmeSimTableDef-classes initializers.

Configure covariate searches and recover archived per-scenario results.

covariateModel(): Creates set of covariate effects
CovariateEffectModel-class CovariateEffectModel: NLME covariate effects model object class
acceptAllEffects(): Accepts all estimates for fixed effects, Sigma, and random effects
SortColumns-class SortColumns: Class initializer for SortColumns
StepwiseParams-class StepwiseParams: Class initializer for NLME StepwiseParams
NlmeScenario-class NlmeScenario: Class initializer for NlmeScenario
listSCMRuns(): List archived covariate search scenarios.
getSCMResults(): Retrieve model and fitmodel-style results for a selected covariate search scenario.
scmSearchTable(): Presentation table for SCM search results

Use these functions to return useful model information.

print(<NlmePmlModel>): Print generic for class NlmePmlModel
isPopulation() `isPopulation<-`(): Get or set whether a model is a population model
modelVariableNames(): Return model variable names
residualEffectNames(): Return residual effect terms available in model
observationNames(): Get observation names
covariateNames(): Return covariate names
doseNames(): Return dose names
extraDoseLines(): Return extra dose lines
extraDoseNames(): Return extra dose names
listCovariateEffectNames(): Lists covariate effect names in the model
secondaryParameterNames(): Get secondary parameter names
structuralParameterNames(): Get structural parameter names
getThetas(): Return theta names and values
getRandomEffectNames(): Return random effect names in model
parsePMLColMap(): Embed column definition info into the model
createModelInfo(): Parse the model and get the list of terms

Built-in datasets.

pkData: Pharmacokinetic dataset containing 16 subjects with single bolus dose
pkpdData: Pharmacokinetic/Pharmacodynamic dataset containing 200 subjects with single bolus dose
pkcovbqlData: Pharmacokinetic pediatric dataset containing 80 subjects with single bolus dose.
OneCpt_IVInfusionData: Pharmacokinetic dataset containing 100 subjects with single dose given by infusion