Generic PK/PD Simulation Platform CAMPSIS

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Documentation for package ‘campsis’ version 1.5.2

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A B C D E F G H I L M N O P R S T U V W Y

-- A --

applyCompartmentCharacteristics Apply compartment characteristics from model. In practice, only compartment infusion duration needs to be applied.
Arm Create a treatment arm.
arm-class Arm class.
arms-class Arms class.

-- B --

BinomialDistribution Binomial distribution.
Bolus Create one or several bolus(es).
bolus-class Bolus class.
Bootstrap Create a bootstrap object.
bootstrap-class Bootstrap class.
BootstrapDistribution Create a bootstrap distribution. During function sampling, CAMPSIS will generate values depending on the given data and arguments.
bootstrap_distribution-class Bootstrap distribution class.

-- C --

campsis_handler Suggested Campsis handler for showing the progress bar.
ConstantDistribution Create a constant distribution. Its value will be constant across all generated samples.
constant_distribution-class Constant distribution class.
convertTime Convert numeric time vector based on the provided units.
Covariate Create a non time-varying (fixed) covariate.
covariate-class Covariate class.
covariates-class Covariates class.

-- D --

Dataset Create a dataset.
dataset-class Dataset class.
DatasetConfig Create a dataset configuration. This configuration allows CAMPSIS to know which are the default depot and observed compartments.
dataset_config-class Dataset configuration class.
days Convert days to hours.
Declare Create declare settings.
declare_settings-class Declare settings class.
DiscreteDistribution Discrete distribution.
distribution-class Distribution class. See this class as an interface.
DoseAdaptation Create a dose adaptation.
dose_adaptation-class Dose adaptation class.
dose_adaptations-class Dose adaptations class.
dosingOnly Filter CAMPSIS output on dosing rows.

-- E --

EtaDistribution Create an ETA distribution. The resulting distribution is a normal distribution, with mean=0 and sd=sqrt(OMEGA).
Event Create an interruption event.
event-class Event class.
EventCovariate Create an event covariate. These covariates can be modified further in interruption events.
Events Create a list of interruption events.
events-class Events class.
event_covariate-class Event covariate class.

-- F --

FixedDistribution Create a fixed distribution. Each sample will be assigned a fixed value coming from vector 'values'.
fixed_covariate-class Fixed covariate class.
fixed_distribution-class Fixed distribution class.
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")).
function_distribution-class Function distribution class.

-- G --

generateIIV Generate IIV matrix for the given Campsis model.
generateIIV_ Generate IIV matrix for the given OMEGA matrix.
getAvailableTimeUnits Return the list of available time units.
getCovariates Get all covariates (fixed / time-varying / event covariates).
getCovariates-method Get all covariates (fixed / time-varying / event covariates).
getEventCovariates Get all event-related covariates.
getEventCovariates-method Get all event-related covariates.
getFixedCovariates Get all fixed covariates.
getFixedCovariates-method Get all fixed covariates.
getIOVs Get all IOV objects.
getIOVs-method Get all IOV objects.
getOccasions Get all occasions.
getOccasions-method Get all occasions.
getSeedForDatasetExport Get seed for dataset export.
getSeedForIteration Get seed for iteration.
getSeedForParametersSampling Get seed for parameter uncertainty sampling.
getSplittingConfiguration Get splitting configuration for parallel export.
getTimes Get all distinct times for the specified object.
getTimes-method Get all distinct times for the specified object.
getTimeVaryingCovariates Get all time-varying covariates.
getTimeVaryingCovariates-method Get all time-varying covariates.

-- H --

Hardware Create hardware settings.
hardware_settings-class Hardware settings class.
hours Convert hours to hours (do nothing).

-- I --

Infusion Create one or several infusion(s).
infusion-class Infusion class.
internal_settings-class Internal settings class (transient object from the simulation settings).
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.

-- L --

length-method Return the number of subjects contained in this arm.
length-method Return the number of subjects contained in this dataset.
LogNormalDistribution Create a log normal distribution.

-- M --

minutes Convert minutes to hours.
months Convert pharma months (1 month = 4 weeks) to hours.
mrgsolve_engine-class mrgsolve engine class.

-- N --

nhanes NHANES database (demographics and body measure data combined, from 2017-2018).
NOCB Create NOCB settings.
nocb_settings-class NOCB settings class.
NormalDistribution Create a normal distribution.

-- O --

Observations Create an observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.
observations-class Observations class.
observations_set-class Observations set class.
obsOnly Filter CAMPSIS output on observation rows.
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.
occasion-class Occasion class.
occasions-class Occasions class.

-- P --

ParameterDistribution Create a parameter distribution. The resulting distribution is a log-normal distribution, with meanlog=log(THETA) and sdlog=sqrt(OMEGA).
PI Compute the prediction interval summary over time.
Progress Create progress settings.
progress_settings-class Progress settings class.
protocol-class Protocol class.

-- R --

retrieveParameterValue Retrieve the parameter value (standardized) for the specified parameter name.
rxode_engine-class RxODE/rxode2 engine class.

-- S --

sample Sample generic object.
sample-method Sample generic object.
scatterPlot Scatter plot (or X vs Y plot).
Scenario Create an scenario.
scenario-class Scenario class.
Scenarios Create a list of scenarios.
scenarios-class Scenarios class.
seconds Convert seconds to hours.
setLabel Set the label.
setLabel-method Set the label.
setSubjects Set the number of subjects.
setSubjects-method Set the number of subjects.
Settings Create advanced simulation 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).
shadedPlot Shaded plot (or prediction interval plot).
simulate Simulate function.
simulate-method Simulate function.
SimulationProgress Create a simulation progress object.
simulation_engine-class Simulation engine class.
simulation_progress-class Simulation progress class.
simulation_settings-class Simulation settings class.
Solver Create solver settings.
solver_settings-class Solver settings class. See ?mrgsolve::update. See ?rxode2::rxSolve.
spaghettiPlot Spaghetti plot.
standardiseTime Standardise time to hours.

-- T --

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.
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.

-- U --

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.
UniformDistribution Create an uniform distribution.

-- V --

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
vpcPlot VPC plot.

-- W --

weeks Convert weeks to hours.

-- Y --

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