Package index • campsis

Dataset elements

Arm(): Create a treatment arm.

Bolus(): Create one or several bolus(es).

Bootstrap(): Create a bootstrap object.

Covariate(): Create a non time-varying (fixed) covariate.

Dataset(): Create a dataset.

DatasetConfig(): Create a dataset configuration. This configuration allows CAMPSIS to know which are the default depot and observed compartments.

DoseAdaptation(): Create a dose adaptation.

EventCovariate(): Create an event covariate. These covariates can be modified further in interruption events.

Infusion(): Create one or several infusion(s).

IOV(): Define inter-occasion variability (IOV) into the dataset. A new variable of name 'colname' will be output into the dataset and will vary at each dose number according to the given distribution.

Observations(): Create an observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.

Occasion(): Define a new occasion. Occasions are defined by mapping occasion values to dose numbers. A new column will automatically be created in the exported dataset.

TimeVaryingCovariate(): Create a time-varying covariate. This covariate will be implemented using EVID=2 rows in the exported dataset and will not use interruption events.

Distributions

BinomialDistribution(): Binomial distribution.

BootstrapDistribution(): Create a bootstrap distribution. During function sampling, CAMPSIS will generate values depending on the given data and arguments.

ConstantDistribution(): Create a constant distribution. Its value will be constant across all generated samples.

DiscreteDistribution(): Discrete distribution.

EtaDistribution(): Create an ETA distribution. The resulting distribution is a normal distribution, with mean=0 and sd=sqrt(OMEGA).

FixedDistribution(): Create a fixed distribution. Each sample will be assigned a fixed value coming from vector 'values'.

FunctionDistribution(): Create a function distribution. During distribution sampling, the provided function will be responsible for generating values for each sample. If first argument of this function is not the size (n), please tell which argument corresponds to the size 'n' (e.g. list(size="n")).

LogNormalDistribution(): Create a log normal distribution.

NormalDistribution(): Create a normal distribution.

ParameterDistribution(): Create a parameter distribution. The resulting distribution is a log-normal distribution, with meanlog=log(THETA) and sdlog=sqrt(OMEGA).

UniformDistribution(): Create an uniform distribution.

Create events

Event(): Create an interruption event.

Events(): Create a list of interruption events.

Create scenarios

Scenario(): Create an scenario.

Scenarios(): Create a list of scenarios.

Sample distributions

sample(): Sample generic object.

Summarise data and plots

PI(): Compute the prediction interval summary over time.

VPC(): Compute the VPC summary. Input data frame must contain the following columns: - replicate: replicate number - low: low percentile value in replicate (and in scenario if present) - med: median value in replicate (and in scenario if present) - up: up percentile value in replicate (and in scenario if present) - any scenario column

scatterPlot(): Scatter plot (or X vs Y plot).

shadedPlot(): Shaded plot (or prediction interval plot).

spaghettiPlot(): Spaghetti plot.

vpcPlot(): VPC plot.

Simulate

simulate(): Simulate function.

Seed utilities

getSeedForDatasetExport(): Get seed for dataset export.

getSeedForIteration(): Get seed for iteration.

getSeedForParametersSampling(): Get seed for parameter uncertainty sampling.

Simulation settings

campsis_handler(): Suggested Campsis handler for showing the progress bar.

Declare(): Create declare settings.

Hardware(): Create hardware settings.

NOCB(): Create NOCB settings.

Outfun(): Create a new output function

Progress(): Create progress settings.

setupPlanDefault(): Setup default plan for the given simulation or hardware settings. This plan will prioritise the distribution of workers in the following order: 1) Replicates (if 'replicate_parallel' is enabled) 2) Scenarios (if 'scenario_parallel' is enabled) 3) Dataset export / slices (if 'dataset_export' or 'slice_parallel' is enabled)

setupPlanSequential(): Setup plan as sequential (i.e. no parallelisation).

Settings(): Create advanced simulation settings.

Solver(): Create solver settings.

SimulationProgress(): Create a simulation progress object.

Time conversion utilities

convertTime(): Convert numeric time vector based on the provided units.

days(): Convert days to hours.

getAvailableTimeUnits(): Return the list of available time units.

hours(): Convert hours to hours (do nothing).

minutes(): Convert minutes to hours.

months(): Convert pharma months (1 month = 4 weeks) to hours.

seconds(): Convert seconds to hours.

standardiseTime(): Standardise time to hours.

weeks(): Convert weeks to hours.

years(): Convert pharma years (1 year = 12*4 weeks) to hours.

For advanced use

dosingOnly(): Filter CAMPSIS output on dosing rows.

generateIIV(): Generate IIV matrix for the given Campsis model.

generateIIV_(): Generate IIV matrix for the given OMEGA matrix.

getCovariates(): Get all covariates (fixed / time-varying / event covariates).

getEventCovariates(): Get all event-related covariates.

getFixedCovariates(): Get all fixed covariates.

getIOVs(): Get all IOV objects.

getOccasions(): Get all occasions.

getSplittingConfiguration(): Get splitting configuration for parallel export.

getTimes(): Get all distinct times for the specified object.

getTimeVaryingCovariates(): Get all time-varying covariates.

length(<arm>): Return the number of subjects contained in this arm.

length(<dataset>): Return the number of subjects contained in this dataset.

nhanes: NHANES database (demographics and body measure data combined, from 2017-2018).

obsOnly(): Filter CAMPSIS output on observation rows.

retrieveParameterValue(): Retrieve the parameter value (standardized) for the specified parameter name.

setLabel(): Set the label.

setSubjects(): Set the number of subjects.

For use in external packages

applyCompartmentCharacteristics(): Apply compartment characteristics from model. In practice, only compartment infusion duration needs to be applied.

Exported classes

arm-class: Arm class.

arms-class: Arms class.

bolus-class: Bolus class.

bootstrap-class: Bootstrap class.

bootstrap_distribution-class: Bootstrap distribution class.

constant_distribution-class: Constant distribution class.

covariate-class: Covariate class.

covariates-class: Covariates class.

dataset-class: Dataset class.

dataset_config-class: Dataset configuration class.

declare_settings-class: Declare settings class.

distribution-class: Distribution class. See this class as an interface.

dose_adaptation-class: Dose adaptation class.

dose_adaptations-class: Dose adaptations class.

event-class: Event class.

event_covariate-class: Event covariate class.

events-class: Events class.

fixed_covariate-class: Fixed covariate class.

fixed_distribution-class: Fixed distribution class.

function_distribution-class: Function distribution class.

hardware_settings-class: Hardware settings class.

infusion-class: Infusion class.

internal_settings-class: Internal settings class (transient object from the simulation settings).

mrgsolve_engine-class: mrgsolve engine class.

nocb_settings-class: NOCB settings class.

protocol-class: Protocol class.

observations-class: Observations class.

observations_set-class: Observations set class.

occasion-class: Occasion class.

occasions-class: Occasions class.

output_function-class: Output function class.

progress_settings-class: Progress settings class.

rxode_engine-class: RxODE/rxode2 engine class.

scenario-class: Scenario class.

scenarios-class: Scenarios class.

simulation_engine-class: Simulation engine class.

simulation_settings-class: Simulation settings class.

simulation_progress-class: Simulation progress class.

solver_settings-class: Solver settings class. See ?mrgsolve::update. See ?rxode2::rxSolve.

time_varying_covariate-class: Time-varying covariate class.

treatment-class: Treatment class.

treatment_iov-class: Treatment IOV class.

treatment_iovs-class: Treatment IOV's class.

undefined_distribution-class: Undefined distribution class. This type of object is automatically created in method toExplicitDistribution() when the user does not provide a concrete distribution. This is because S4 objects do not accept NULL values.