Package: campsis 1.5.5

Nicolas Luyckx

campsis: Generic PK/PD Simulation Platform CAMPSIS

A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R packages 'rxode2' and 'mrgsolve'. CAMPSIS provides an abstraction layer over the underlying processes of writing a PK/PD model, assembling a custom dataset and running a simulation. CAMPSIS has a strong dependency to the R package 'campsismod', which allows to read/write a model from/to files and adapt it further on the fly in the R environment. Package 'campsis' allows the user to assemble a dataset in an intuitive manner. Once the user’s dataset is ready, the package is in charge of preparing the simulation, calling 'rxode2' or 'mrgsolve' (at the user's choice) and returning the results, for the given model, dataset and desired simulation settings.

Authors:Nicolas Luyckx [aut, cre]

campsis_1.5.5.tar.gz
campsis_1.5.5.tar.gz(r-4.5-noble)campsis_1.5.5.tar.gz(r-4.4-noble)
campsis_1.5.5.tgz(r-4.4-emscripten)campsis_1.5.5.tgz(r-4.3-emscripten)
campsis.pdf |campsis.html✨
campsis/json (API)
NEWS

# Install 'campsis' in R:
install.packages('campsis', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/calvagone/campsis/issues

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

5.63 score 94 scripts 346 downloads 78 exports 58 dependencies

Last updated 13 hours agofrom:98ee544759. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 26 2024
R-4.5-linuxOKNov 26 2024

Exports:applyScenarioArmBinomialDistributionBolusBootstrapBootstrapDistributioncampsis_handlerConstantDistributionconvertTimeCovariateDatasetDatasetConfigdaysDeclareDiscreteDistributionDoseAdaptationdosingOnlyEtaDistributionEventEventCovariateEventsFixedDistributionFunctionDistributiongenerateIIVgenerateIIV_getAvailableTimeUnitsgetCovariatesgetEventCovariatesgetFixedCovariatesgetIOVsgetOccasionsgetSeedForDatasetExportgetSeedForIterationgetSeedForParametersSamplinggetSplittingConfigurationgetTimesgetTimeVaryingCovariatesHardwarehoursInfusionIOVLogNormalDistributionminutesmonthsNOCBNormalDistributionObservationsobsOnlyOccasiononCIonCranOutfunParameterDistributionPIProgressretrieveParameterValuesamplescatterPlotScenarioScenariossecondssetLabelsetSubjectsSettingssetupPlanDefaultsetupPlanSequentialshadedPlotsimulateSimulationProgressSolverspaghettiPlotstandardiseTimeTimeVaryingCovariateUniformDistributionVPCvpcPlotweeksyears

Dependencies:assertthatbitbit64campsismodclicliprcodetoolscolorspacecpp11crayondigestdplyrfansifarverfurrrfuturegenericsggplot2globalsgluegtablehmsisobandlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmeparallellypillarpkgconfigplyrprettyunitsprogressprogressrpurrrR6RColorBrewerRcppreadrrlangscalesstringistringrtibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr

Bioavailability

Rendered fromv04_bioavailability.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Complex PK/PD models from literature

Rendered fromv14_complex_pkpd_models.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
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Covariates

Rendered fromv03_covariates.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
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Create your dataset

Rendered fromv01_dataset.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
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Get started with CAMPSIS

Rendered fromcampsis.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2023-10-13
Started: 2022-02-16

Infusions

Rendered fromv06_infusions.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Initial conditions

Rendered fromv08_initial_conditions.Rmdusingknitr::rmarkdownon Nov 26 2024.

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Started: 2024-11-26

Inter-occasion variability

Rendered fromv07_iov.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Interruption events

Rendered fromv13_events.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Lag time

Rendered fromv05_lag_time.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
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PK/PD model library

Rendered fromv15_pkpd_model_library.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
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Progress bar

Rendered fromv16_progress_bar.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
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Replicate your study

Rendered fromv10_replicate_study.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Run your simulation in parallel

Rendered fromv17_run_simulation_in_parallel.Rmdusingknitr::rmarkdownon Nov 26 2024.

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Scenarios

Rendered fromv11_scenarios.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
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Simple dose adaptations

Rendered fromv09_dose_adaptation.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Time-varying covariates

Rendered fromv12_time_varying_covariates.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Variability levels

Rendered fromv02_uncertainties.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-11-26
Started: 2024-11-26

Readme and manuals

Help Manual

Help pageTopics
Apply compartment characteristics from model. In practice, only compartment infusion duration needs to be applied.applyCompartmentCharacteristics
Create a treatment arm.Arm
Arm class.arm-class
Arms class.arms-class
Binomial distribution.BinomialDistribution
Create one or several bolus(es).Bolus
Bolus class.bolus-class
Create a bootstrap object.Bootstrap
Bootstrap distribution class.bootstrap_distribution-class
Bootstrap class.bootstrap-class
Create a bootstrap distribution. During function sampling, CAMPSIS will generate values depending on the given data and arguments.BootstrapDistribution
Suggested Campsis handler for showing the progress bar.campsis_handler
Constant distribution class.constant_distribution-class
Create a constant distribution. Its value will be constant across all generated samples.ConstantDistribution
Convert numeric time vector based on the provided units.convertTime
Create a non time-varying (fixed) covariate.Covariate
Covariate class.covariate-class
Covariates class.covariates-class
Create a dataset.Dataset
Dataset configuration class.dataset_config-class
Dataset class.dataset-class
Create a dataset configuration. This configuration allows CAMPSIS to know which are the default depot and observed compartments.DatasetConfig
Convert days to hours.days
Create declare settings.Declare
Declare settings class.declare_settings-class
Discrete distribution.DiscreteDistribution
Distribution class. See this class as an interface.distribution-class
Dose adaptation class.dose_adaptation-class
Dose adaptations class.dose_adaptations-class
Create a dose adaptation.DoseAdaptation
Filter CAMPSIS output on dosing rows.dosingOnly
Create an ETA distribution. The resulting distribution is a normal distribution, with mean=0 and sd=sqrt(OMEGA).EtaDistribution
Create an interruption event.Event
Event covariate class.event_covariate-class
Event class.event-class
Create an event covariate. These covariates can be modified further in interruption events.EventCovariate
Create a list of interruption events.Events
Events class.events-class
Fixed covariate class.fixed_covariate-class
Fixed distribution class.fixed_distribution-class
Create a fixed distribution. Each sample will be assigned a fixed value coming from vector 'values'.FixedDistribution
Function distribution class.function_distribution-class
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")).FunctionDistribution
Generate IIV matrix for the given Campsis model.generateIIV
Generate IIV matrix for the given OMEGA matrix.generateIIV_
Return the list of available time units.getAvailableTimeUnits
Get all covariates (fixed / time-varying / event covariates).getCovariates getCovariates,arm-method getCovariates,arms-method getCovariates,covariates-method getCovariates,dataset-method
Get all event-related covariates.getEventCovariates getEventCovariates,arm-method getEventCovariates,arms-method getEventCovariates,covariates-method getEventCovariates,dataset-method
Get all fixed covariates.getFixedCovariates getFixedCovariates,arm-method getFixedCovariates,arms-method getFixedCovariates,covariates-method getFixedCovariates,dataset-method
Get all IOV objects.getIOVs getIOVs,arm-method getIOVs,arms-method getIOVs,dataset-method
Get all occasions.getOccasions getOccasions,arm-method getOccasions,arms-method getOccasions,dataset-method
Get seed for dataset export.getSeedForDatasetExport
Get seed for iteration.getSeedForIteration
Get seed for parameter uncertainty sampling.getSeedForParametersSampling
Get splitting configuration for parallel export.getSplittingConfiguration
Get all distinct times for the specified object.getTimes getTimes,arm-method getTimes,arms-method getTimes,dataset-method getTimes,events-method getTimes,observations_set-method
Get all time-varying covariates.getTimeVaryingCovariates getTimeVaryingCovariates,arm-method getTimeVaryingCovariates,arms-method getTimeVaryingCovariates,covariates-method getTimeVaryingCovariates,dataset-method
Create hardware settings.Hardware
Hardware settings class.hardware_settings-class
Convert hours to hours (do nothing).hours
Create one or several infusion(s).Infusion
Infusion class.infusion-class
Internal settings class (transient object from the simulation settings).internal_settings-class
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.IOV
Return the number of subjects contained in this arm.length,arm-method
Return the number of subjects contained in this dataset.length,dataset-method
Create a log normal distribution.LogNormalDistribution
Convert minutes to hours.minutes
Convert pharma months (1 month = 4 weeks) to hours.months
mrgsolve engine class.mrgsolve_engine-class
NHANES database (demographics and body measure data combined, from 2017-2018).nhanes
Create NOCB settings.NOCB
NOCB settings class.nocb_settings-class
Create a normal distribution.NormalDistribution
Create an observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.Observations
Observations set class.observations_set-class
Observations class.observations-class
Filter CAMPSIS output on observation rows.obsOnly
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
Occasion class.occasion-class
Occasions class.occasions-class
Create a new output functionOutfun
Output function class.output_function-class
Create a parameter distribution. The resulting distribution is a log-normal distribution, with meanlog=log(THETA) and sdlog=sqrt(OMEGA).ParameterDistribution
Compute the prediction interval summary over time.PI
Create progress settings.Progress
Progress settings class.progress_settings-class
Protocol class.protocol-class
Retrieve the parameter value (standardized) for the specified parameter name.retrieveParameterValue
RxODE/rxode2 engine class.rxode_engine-class
Sample generic object.sample sample,bolus,integer-method sample,bootstrap,integer-method sample,bootstrap_distribution,integer-method sample,campsis_model,integer-method sample,constant_distribution,integer-method sample,covariate,integer-method sample,fixed_distribution,integer-method sample,function_distribution,integer-method sample,infusion,integer-method sample,observations,integer-method
Scatter plot (or X vs Y plot).scatterPlot
Create an scenario.Scenario
Scenario class.scenario-class
Create a list of scenarios.Scenarios
Scenarios class.scenarios-class
Convert seconds to hours.seconds
Set the label.setLabel setLabel,arm,character-method
Set the number of subjects.setSubjects setSubjects,arm,integer-method setSubjects,dataset,integer-method
Create advanced simulation settings.Settings
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)setupPlanDefault
Setup plan as sequential (i.e. no parallelisation).setupPlanSequential
Shaded plot (or prediction interval plot).shadedPlot
Simulate function.simulate simulate,campsis_model,data.frame,character,events,scenarios,function,character,output_function,integer,integer,logical,simulation_settings-method simulate,campsis_model,dataset,character,events,scenarios,function,character,output_function,integer,integer,logical,simulation_settings-method simulate,campsis_model,tbl_df,character,events,scenarios,function,character,output_function,integer,integer,logical,simulation_settings-method simulate,campsis_model,tbl_df,mrgsolve_engine,events,scenarios,function,character,output_function,integer,integer,logical,simulation_settings-method simulate,campsis_model,tbl_df,rxode_engine,events,scenarios,function,character,output_function,integer,integer,logical,simulation_settings-method
Simulation engine class.simulation_engine-class
Simulation progress class.simulation_progress-class
Simulation settings class.simulation_settings-class
Create a simulation progress object.SimulationProgress
Create solver settings.Solver
Solver settings class. See ?mrgsolve::update. See ?rxode2::rxSolve.solver_settings-class
Spaghetti plot.spaghettiPlot
Standardise time to hours.standardiseTime
Time-varying covariate class.time_varying_covariate-class
Create a time-varying covariate. This covariate will be implemented using EVID=2 rows in the exported dataset and will not use interruption events.TimeVaryingCovariate
Treatment IOV class.treatment_iov-class
Treatment IOV's class.treatment_iovs-class
Treatment class.treatment-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.undefined_distribution-class
Create an uniform distribution.UniformDistribution
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 columnVPC
VPC plot.vpcPlot
Convert weeks to hours.weeks
Convert pharma years (1 year = 12*4 weeks) to hours.years