Create a treatment arm.

Description

Create a treatment arm.

Usage

Arm(id = as.integer(NA), subjects = 1, label = as.character(NA))

Arguments

Value

an arm

Binomial distribution.

Description

Binomial distribution.

Usage

BinomialDistribution(trials, prob)

Arguments

Value

a binomial distribution

Create one or several bolus(es).

Description

Create one or several bolus(es).

Usage

Bolus(
  time,
  amount,
  compartment = NULL,
  f = NULL,
  lag = NULL,
  ii = NULL,
  addl = NULL,
  wrap = TRUE,
  ref = NULL,
  rep = NULL
)

Arguments

Value

a single bolus or a list of boluses

Create a bootstrap object.

Description

Create a bootstrap object.

Usage

Bootstrap(
  data,
  id = "BS_ID",
  replacement = FALSE,
  random = FALSE,
  export_id = FALSE
)

Arguments

Value

a bootstrap object

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

Description

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

Usage

BootstrapDistribution(data, replacement = FALSE, random = FALSE)

Arguments

Value

a bootstrap distribution

Create a constant distribution. Its value will be constant across all generated samples.

Description

Create a constant distribution. Its value will be constant across all generated samples.

Usage

ConstantDistribution(value)

Arguments

Value

a constant distribution (same value for all samples)

Create a non time-varying (fixed) covariate.

Description

Create a non time-varying (fixed) covariate.

Usage

Covariate(name, distribution)

Arguments

Value

a fixed covariate

Cyclic schedule constructor.

Description

Cyclic schedule constructor.

Usage

CyclicSchedule(duration, repetitions)

Arguments

Value

a cyclic schedule

Create a dataset.

Description

Create a dataset.

Usage

Dataset(subjects = NULL, label = as.character(NA))

Arguments

Value

a dataset

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

Description

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

Usage

DatasetConfig(
  defDepotCmt = 1,
  defObsCmt = 1,
  exportTSLD = FALSE,
  exportTDOS = FALSE,
  timeUnitDataset = "hour",
  timeUnitExport = "hour"
)

Arguments

Value

a dataset configuration

Create a dataset summary (internal method).

Description

Create a dataset summary (internal method).

Usage

DatasetSummary()

Value

a dataset summary

Create declare settings.

Description

Create declare settings.

Usage

Declare(variables = character(0))

Arguments

Value

Declare settings

Discrete distribution.

Description

Discrete distribution.

Usage

DiscreteDistribution(x, prob, replace = TRUE)

Arguments

Value

a discrete distribution

Create a dose adaptation.

Description

Create a dose adaptation.

Usage

DoseAdaptation(formula, compartments = NULL)

Arguments

Value

a fixed covariate

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

Description

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

Usage

EtaDistribution(model, omega)

Arguments

Value

an ETA distribution

Create an interruption event.

Description

Create an interruption event.

Usage

Event(name = NULL, times, fun, debug = FALSE)

Arguments

Value

an event definition

Create an event covariate. These covariates can be modified further in interruption events.

Description

Create an event covariate. These covariates can be modified further in interruption events.

Usage

EventCovariate(name, distribution)

Arguments

Value

a time-varying covariate

Create an event iteration object.

Description

Create an event iteration object.

Usage

EventIteration(index, start, end, inits = data.frame(), maxIndex)

Arguments

Value

an event iteration object

Create an event-related observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.

Description

Create an event-related observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.

Usage

EventRelatedObservations(times, compartment = NA)

Arguments

Value

observations

Create a list of interruption events.

Description

Create a list of interruption events.

Usage

Events()

Value

a events object

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

Description

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

Usage

FixedDistribution(values)

Arguments

Value

a fixed distribution (1 value per sample)

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")).

Description

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")).

Usage

FunctionDistribution(fun, args)

Arguments

Value

a function distribution

Create hardware settings.

Description

Create hardware settings.

Usage

Hardware(
  cpu = 1,
  replicate_parallel = FALSE,
  scenario_parallel = FALSE,
  slice_parallel = FALSE,
  slice_size = NULL,
  dataset_parallel = FALSE,
  dataset_slice_size = 500,
  auto_setup_plan = NULL
)

Arguments

Value

hardware settings

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.

Description

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.

Usage

IOV(colname, distribution, doseNumbers = NULL)

Arguments

Value

an IOV object

Create one or several infusion(s).

Description

Create one or several infusion(s).

Usage

Infusion(
  time,
  amount,
  compartment = NULL,
  f = NULL,
  lag = NULL,
  duration = NULL,
  rate = NULL,
  ii = NULL,
  addl = NULL,
  wrap = TRUE,
  ref = NULL,
  rep = NULL
)

Arguments

Value

a single infusion or a list of infusions.

Create a log normal distribution.

Description

Create a log normal distribution.

Usage

LogNormalDistribution(meanlog, sdlog)

Arguments

Value

a log normal distribution

Create NOCB settings.

Description

Create NOCB settings.

Usage

NOCB(enable = NULL, variables = character(0))

Arguments

Value

NOCB settings

Create a normal distribution.

Description

Create a normal distribution.

Usage

NormalDistribution(mean, sd)

Arguments

Value

a normal distribution

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

Description

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

Usage

Observations(times, compartment = NA)

Arguments

Value

an observations list

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.

Description

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.

Usage

Occasion(colname, values, doseNumbers)

Arguments

Value

occasion object

Create a new output function

Description

Create a new output function

Usage

Outfun(
  fun = function(x, ...) {
     x
 },
  args = list(),
  packages = NULL,
  level = "scenario"
)

Arguments

Value

an output function

Compute the prediction interval summary over time.

Description

Compute the prediction interval summary over time.

Usage

PI(x, output, scenarios = NULL, level = 0.9, gather = TRUE)

Arguments

Value

a summary table

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

Description

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

Usage

ParameterDistribution(model, theta, omega = NULL)

Arguments

Value

a parameter distribution

Create progress settings.

Description

Create progress settings.

Usage

Progress(tick_slice = TRUE)

Arguments

Value

progress settings

'Repeat at' schedule constructor. Note that the time 0 for the base pattern will be added by default if not provided.

Description

'Repeat at' schedule constructor. Note that the time 0 for the base pattern will be added by default if not provided.

Usage

RepeatAtSchedule(times)

Arguments

Value

a 'repeat-at' schedule

Create an scenario.

Description

Create an scenario.

Usage

Scenario(name = NULL, model = NULL, dataset = NULL)

Arguments

Value

a new scenario

Create a list of scenarios.

Description

Create a list of scenarios.

Usage

Scenarios()

Value

a scenarios object

Create advanced simulation settings.

Description

Create advanced simulation settings.

Usage

Settings(...)

Arguments

Value

advanced simulation settings

Create a simulation progress object.

Description

Create a simulation progress object.

Usage

SimulationProgress(
  replicates = 1,
  scenarios = 1,
  progressor = NULL,
  hardware = NULL
)

Arguments

Value

a progress bar

Create solver settings.

Description

Create solver settings.

Usage

Solver(
  atol = 1e-08,
  rtol = 1e-08,
  hmax = NA,
  maxsteps = 70000L,
  method = "liblsoda"
)

Arguments

Value

solver settings

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

Description

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

Usage

TimeVaryingCovariate(name, table)

Arguments

Value

a time-varying covariate

Create an uniform distribution.

Description

Create an uniform distribution.

Usage

UniformDistribution(min, max)

Arguments

Value

an uniform distribution

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

Description

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

Usage

VPC(x, scenarios = NULL, level = 0.9)

Arguments

Value

VPC summary with columns TIME, <scenarios> and all combinations of low, med, up (i.e. low_low, low_med, low_up, etc.)

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

Description

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

Usage

applyCompartmentCharacteristics(table, properties)

Arguments

Value

updated dataset

Apply scenario to the given model or dataset.

Description

Apply scenario to the given model or dataset.

Usage

applyScenario(x, scenario)

Arguments

Value

an updated model or dataset

Arm class.

Description

Arm class.

Slots

id

arm unique ID, integer

subjects

number of subjects in arm, integer

label

arm label, single character string

protocol

protocol

covariates

covariates

bootstrap

covariates to be bootstrapped

Arms class.

Description

Arms class.

Assign dose number to each treatment entry.

Description

Assign dose number to each treatment entry.

Usage

assignDoseNumber(object)

Arguments

Value

updated treatment object

Bolus class.

Description

Bolus class.

Bolus wrapper class.

Description

Bolus wrapper class.

Bootstrap class.

Description

Bootstrap class.

Slots

data

data frame to be bootstrapped. Column 'BS_ID' is mandatory and corresponds to the original row ID from the bootstrap. It must be numeric and unique. Other columns are covariates to be bootstrapped (row by row).

replacement

values can be reused or not, logical

random

values are drawn randomly, logical

export_id

tell CAMPSIS if 'BS_ID' must be exported into the dataset, logical

Bootstrap distribution class.

Description

Bootstrap distribution class.

Slots

data

values to draw, numeric vector

replacement

values can be reused or not, logical

random

values are drawn randomly, logical

Suggested Campsis handler for showing the progress bar.

Description

Suggested Campsis handler for showing the progress bar.

Usage

campsis_handler()

Value

a progressr handler list

Check ii and addl arguments in addition to time.

Description

Check ii and addl arguments in addition to time.

Usage

checkIIandADDL(time, ii, addl)

Arguments

Value

no return value

Compute incremental progress.

Description

Compute incremental progress.

Usage

computeIncrementalProgress(object, tick_slice)

Arguments

Value

incremental progress, in percentage

Constant distribution class.

Description

Constant distribution class.

Slots

value

covariate value, single numeric value

Convert numeric time vector based on the provided units.

Description

Convert numeric time vector based on the provided units.

Usage

convertTime(x, from, to)

Arguments

Value

numeric vector with the converted times

Counter-balance LOCF mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the left (by one).

Description

Counter-balance LOCF mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the left (by one).

Usage

counterBalanceLocfMode(table, columnNames)

Arguments

Value

2-dimensional dataset

Counter-balance NOCB mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the right (by one).

Description

Counter-balance NOCB mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the right (by one).

Usage

counterBalanceNocbMode(table, columnNames)

Arguments

Value

2-dimensional dataset

Covariate class.

Description

Covariate class.

Slots

name

covariate name, single character value

distribution

covariate distribution

Covariates class.

Description

Covariates class.

Cut table according to given iteration.

Description

Cut table according to given iteration.

Usage

cutTableForEvent(table, iteration, summary)

Arguments

Value

a data frame

Cyclic schedule class.

Description

Cyclic schedule class.

Slots

duration

duration of the cycle, numeric value

repetitions

number of additional repetitions to the base pattern, integer value

Dataset class.

Description

Dataset class.

Slots

arms

a list of treatment arms

config

dataset configuration for export

iiv

data frame containing the inter-individual variability (all ETAS) for the export

Dataset configuration class.

Description

Dataset configuration class.

Slots

def_depot_cmt

default depot compartment, integer

def_obs_cmt

default observation compartment, integer

export_tsld

export column TSLD, logical

export_tdos

export column TDOS, logical

time_unit_dataset

unit of time in dataset, character ('hour' by default)

time_unit_export

unit of time in export, character ('hour' by default)

Convert days to hours.

Description

Convert days to hours.

Usage

days(x)

Arguments

Value

numeric vector in hours

Declare settings class.

Description

Declare settings class.

Slots

variables

uninitialized variables to be declared, only needed with mrgsolve

Distribution class. See this class as an interface.

Description

Distribution class. See this class as an interface.

Dose adaptation class.

Description

Dose adaptation class.

Slots

formula

formula to apply, single character string, e.g. "AMT*WT"

compartments

compartment numbers where the formula needs to be applied

Dose adaptations class.

Description

Dose adaptations class.

Filter CAMPSIS output on dosing rows.

Description

Filter CAMPSIS output on dosing rows.

Usage

dosingOnly(x)

Arguments

Value

a data frame with the dosing rows

Return the 'DROP_OTHERS' string that may be used in the 'outvars' vector for RxODE/mrgsolve to drop all others variables that are usually output in the resulting data frame.

Description

Return the 'DROP_OTHERS' string that may be used in the 'outvars' vector for RxODE/mrgsolve to drop all others variables that are usually output in the resulting data frame.

Usage

dropOthers()

Value

a character value

Event class.

Description

Event class.

Slots

name

event name, character value

times

interruption times, numeric vector

fun

event function to apply at each interruption

debug

output the variables that were changed through this event

Event covariate class.

Description

Event covariate class.

Events class.

Description

Events class.

Export delegate method. This method is common to RxODE and mrgsolve.

Description

Export delegate method. This method is common to RxODE and mrgsolve.

Usage

exportDelegate(object, dest, model, arm_offset = NULL, offset_within_arm = 0)

Arguments

Value

2-dimensional dataset, same for RxODE and mrgsolve

Export table delegate.

Description

Export table delegate.

Usage

exportTableDelegate(model, dataset, dest, events, seed, tablefun, settings)

Arguments

Value

a data frame

Factor scenarios columns if not done yet.

Description

Factor scenarios columns if not done yet.

Usage

factorScenarios(x, scenarios = NULL)

Arguments

Fill IOV/Occasion columns.

Description

Problem in RxODE (LOCF mode) / mrgsolve (LOCF mode), if 2 rows have the same time (often: OBS then DOSE), first row covariate value is taken! Workaround: identify these rows (group by ID and TIME) and apply a fill in the UP direction.

Usage

fillIOVOccColumns(table, columnNames, downDirectionFirst)

Arguments

Value

2-dimensional dataset

Fixed covariate class.

Description

Fixed covariate class.

Fixed distribution class.

Description

Fixed distribution class.

Slots

values

covariate values, numeric vector (1 value per sample)

Function distribution class.

Description

Function distribution class.

Slots

fun

function name, character (e.g. 'rnorm')

args

list of arguments (e.g list(mean=70, sd=10))

Generate IIV matrix for the given Campsis model.

Description

Generate IIV matrix for the given Campsis model.

Usage

generateIIV(model, n, offset = 0)

Arguments

Value

IIV data frame with ID column

Generate IIV matrix for the given OMEGA matrix.

Description

Generate IIV matrix for the given OMEGA matrix.

Usage

generateIIV_(omega, n)

Arguments

Value

IIV data frame

Return the list of available time units.

Description

Return the list of available time units.

Usage

getAvailableTimeUnits()

Value

character vector

Get data of given column unless if does not exist (return NULL in that case).

Description

Get data of given column unless if does not exist (return NULL in that case).

Usage

getColumn(.data, colname)

Arguments

Value

a vector

Get a mapping table with all possibilities of compartment names and their indexes knowing that compartment names can be provided as character or as integer.

Description

Get a mapping table with all possibilities of compartment names and their indexes knowing that compartment names can be provided as character or as integer.

Usage

getCompartmentMapping(compartments)

Arguments

Value

a tibble with two columns: INDEX and NAME

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

Description

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

Usage

getCovariates(object)

## S4 method for signature 'covariates'
getCovariates(object)

## S4 method for signature 'arm'
getCovariates(object)

## S4 method for signature 'arms'
getCovariates(object)

## S4 method for signature 'dataset'
getCovariates(object)

Arguments

Value

all covariates from object

Get dataset max time.

Description

Get dataset max time.

Usage

getDatasetMaxTime(dataset)

Arguments

Value

max time of dataset, whatever its form, 2-dimensional or structured

Get all event-related covariates.

Description

Get all event-related covariates.

Usage

getEventCovariates(object)

## S4 method for signature 'covariates'
getEventCovariates(object)

## S4 method for signature 'arm'
getEventCovariates(object)

## S4 method for signature 'arms'
getEventCovariates(object)

## S4 method for signature 'dataset'
getEventCovariates(object)

Arguments

Value

all event-related covariates from object

Get list of event iterations.

Description

Get list of event iterations.

Usage

getEventIterations(events, maxTime)

Arguments

Value

a list of event iterations

Get all fixed covariates.

Description

Get all fixed covariates.

Usage

getFixedCovariates(object)

## S4 method for signature 'covariates'
getFixedCovariates(object)

## S4 method for signature 'arm'
getFixedCovariates(object)

## S4 method for signature 'arms'
getFixedCovariates(object)

## S4 method for signature 'dataset'
getFixedCovariates(object)

Arguments

Value

all fixed covariates from object

Get scheduling mode for furrr (see argument 'scheduling' available in furrr options).

Description

Get scheduling mode for furrr (see argument 'scheduling' available in furrr options).

Usage

getFurrrScheduling(parallel)

Arguments

Value

1 for parallel computing, 0 otherwise

Get all IOV objects.

Description

Get all IOV objects.

Usage

getIOVs(object)

## S4 method for signature 'arm'
getIOVs(object)

## S4 method for signature 'arms'
getIOVs(object)

## S4 method for signature 'dataset'
getIOVs(object)

Arguments

Value

all IOV's from object

Get initial conditions at simulation start-up.

Description

Get initial conditions at simulation start-up.

Usage

getInitialConditions(subdataset, iteration, cmtNames)

Arguments

Value

named numeric vector with the new initial conditions

Get all occasions.

Description

Get all occasions.

Usage

getOccasions(object)

## S4 method for signature 'arm'
getOccasions(object)

## S4 method for signature 'arms'
getOccasions(object)

## S4 method for signature 'dataset'
getOccasions(object)

Arguments

Value

all occasions from object

Get random seed value.

Description

Get random seed value.

Usage

getRandomSeedValue()

Value

random seed value generated based on time

Get seed value.

Description

Get seed value.

Usage

getSeed(seed = NULL)

Arguments

Value

a seed value, integer

Get seed for dataset export.

Description

Get seed for dataset export.

Usage

getSeedForDatasetExport(seed, progress)

Arguments

Value

the seed value used to export the dataset

Get seed for iteration.

Description

Get seed for iteration.

Usage

getSeedForIteration(seed, progress)

Arguments

Value

the seed value to be used for the given replicate number and iteration

Get seed for parameter uncertainty sampling.

Description

Get seed for parameter uncertainty sampling.

Usage

getSeedForParametersSampling(seed)

Arguments

Value

the seed value used to sample parameter uncertainty

Get simulation engine type.

Description

Get simulation engine type.

Usage

getSimulationEngineType(dest)

Arguments

Value

simulation engine type

Get splitting configuration for parallel export.

Description

Get splitting configuration for parallel export.

Usage

getSplittingConfiguration(dataset, hardware)

Arguments

Value

splitting configuration list (if 'parallel_dataset' is enabled) or NA (if 'parallel_dataset' disabled or if the length of the dataset is less than the dataset export slice size)

Get all time-varying covariates.

Description

Get all time-varying covariates.

Usage

getTimeVaryingCovariates(object)

## S4 method for signature 'covariates'
getTimeVaryingCovariates(object)

## S4 method for signature 'arm'
getTimeVaryingCovariates(object)

## S4 method for signature 'arms'
getTimeVaryingCovariates(object)

## S4 method for signature 'dataset'
getTimeVaryingCovariates(object)

Arguments

Value

all time-varying covariates from object

Get all time-varying variables. These variables are likely to be influenced by the NOCB mode chosen and by the 'nocbvars' vector.

Description

Get all time-varying variables. These variables are likely to be influenced by the NOCB mode chosen and by the 'nocbvars' vector.

Usage

getTimeVaryingVariables(object)

Arguments

Value

character vector with all time-varying variables of the dataset

Get all distinct times for the specified object.

Description

Get all distinct times for the specified object.

Usage

getTimes(object)

## S4 method for signature 'observations_set'
getTimes(object)

## S4 method for signature 'arm'
getTimes(object)

## S4 method for signature 'arms'
getTimes(object)

## S4 method for signature 'events'
getTimes(object)

## S4 method for signature 'dataset'
getTimes(object)

Arguments

Value

numeric vector with all unique times, sorted

Hardware settings class.

Description

Hardware settings class.

Slots

cpu

number of CPU cores to use, default is 1

replicate_parallel

enable parallel computing for replicates, default is FALSE

scenario_parallel

enable parallel computing for scenarios, default is FALSE

slice_parallel

enable parallel computing for slices, default is FALSE

slice_size

number of subjects per simulated slice, default is NULL (auto-configured by Campsis depending on the specified engine)

dataset_parallel

enable parallelisation when exporting dataset into a table, default is FALSE

dataset_slice_size

dataset slice size when exporting subjects to a table, default is 500. Only applicable if 'dataset_parallel' is enabled.

auto_setup_plan

auto-setup plan with the library future, default is FALSE

Convert hours to hours (do nothing).

Description

Convert hours to hours (do nothing).

Usage

hours(x)

Arguments

Value

numeric vector in hours

Import the whole campsismod package into NAMESPACE when parsed by 'roxygen'.

Description

Import the whole campsismod package into NAMESPACE when parsed by 'roxygen'.

Usage

importCampsismodToNamespace()

Value

always TRUE

Infusion class.

Description

Infusion class.

Slots

duration

infusion duration, distribution list

rate

infusion rate, distribution list

Infusion wrapper class.

Description

Infusion wrapper class.

Internal settings class (transient object from the simulation settings).

Description

Internal settings class (transient object from the simulation settings).

Slots

dataset_summary

dataset summary

progress

simulation progress

iterations

list of event iterations

Is the given bootstrap empty.

Description

Is the given bootstrap empty.

Usage

isEmptyBootstrap(object)

Arguments

Value

logical value TRUE/FALSE

Left-join IIV matrix.

Description

Left-join IIV matrix.

Usage

leftJoinIIV(table, iiv)

Arguments

Value

updated table with IIV matrix

Return the number of subjects contained in this arm.

Description

Return the number of subjects contained in this arm.

Usage

## S4 method for signature 'arm'
length(x)

Arguments

Value

a number

Return the number of repetition cycles.

Description

Return the number of repetition cycles.

Usage

## S4 method for signature 'cyclic_schedule'
length(x)

Arguments

Value

a number

Return the number of subjects contained in this dataset.

Description

Return the number of subjects contained in this dataset.

Usage

## S4 method for signature 'dataset'
length(x)

Arguments

Value

a number

Return the number of repetition cycles.

Description

Return the number of repetition cycles.

Usage

## S4 method for signature 'repeat_at_schedule'
length(x)

Arguments

Value

a number

Merge time-varying covariates into a single data frame. This last data frame will be merged afterwards with all treatment and observation rows.

Description

Merge time-varying covariates into a single data frame. This last data frame will be merged afterwards with all treatment and observation rows.

Usage

mergeTimeVaryingCovariates(covariates, ids_within_arm, arm_offset)

Arguments

Value

a data.frame

Convert minutes to hours.

Description

Convert minutes to hours.

Usage

minutes(x)

Arguments

Value

numeric vector in hours

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

Description

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

Usage

months(x)

Arguments

Value

numeric vector in hours

mrgsolve engine class.

Description

mrgsolve engine class.

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

Description

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

Usage

nhanes

Format

data frame

BS_ID

Original identifier

SEX

Sex: 1 for males, 2 for females

AGE

Age in years

BW

Body weight in kg

BMI

Body mass index

HT

Height in cm

Source

https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DEMO_J.XPT

https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/BMX_J.XPT

NOCB settings class.

Description

NOCB settings class.

Slots

enable

enable/disable next-observation carried backward mode (NOCB), default value is TRUE for mrgsolve, FALSE for RxODE

variables

variable names subject to NOCB behavior (see vignette for more info)

Filter CAMPSIS output on observation rows.

Description

Filter CAMPSIS output on observation rows.

Usage

obsOnly(x)

Arguments

Value

a data frame with the observation rows

Observations class.

Description

Observations class.

Slots

times

observation times, numeric vector

compartment

compartment index (integer) or name (character)

dv

observed values, numeric vector (FOR EXTERNAL USE)

Observations set class.

Description

Observations set class.

Occasion class.

Description

Occasion class.

Slots

colname

single character value representing the column name related to this occasion

values

occasion values, integer vector, same length as dose_numbers

dose_numbers

associated dose numbers, integer vector, same length as values

Occasions class.

Description

Occasions class.

Check if the current session is on CI (e.g. GitHub actions).

Description

Check if the current session is on CI (e.g. GitHub actions).

Usage

onCI()

Value

logical value TRUE/FALSE

Check if the current session is on CRAN. The objective is to potentially suppress long tasks to be run on CRAN (long tests or vignettes).

Description

Check if the current session is on CRAN. The objective is to potentially suppress long tasks to be run on CRAN (long tests or vignettes).

Usage

onCran()

Value

logical value TRUE/FALSE

Output function class.

Description

Output function class.

Slots

fun

function or purrr-style lambda formula, first argument 'x' must be the results

args

extra arguments, named list

packages

packages that must be loaded to execute the given function, character vector

level

either 'scenario' or 'replicate'. Default is 'scenario'.

Preprocess ARM column. Add ARM equation in model automatically.

Description

Preprocess ARM column. Add ARM equation in model automatically.

Usage

preprocessArmColumn(dataset, model)

Arguments

Value

updated model

Pre-process destination engine. Throw an error message if the destination engine is not installed.

Description

Pre-process destination engine. Throw an error message if the destination engine is not installed.

Usage

preprocessDest(dest)

Arguments

Value

'rxode2', 'RxODE' or 'mrgsolve'

Preprocess 'dosing' argument.

Description

Preprocess 'dosing' argument.

Usage

preprocessDosing(dosing)

Arguments

Value

user value, if not specified, return FALSE (observations only)

Pre-process events.

Description

Pre-process events.

Usage

preprocessEvents(events)

Arguments

Preprocess subjects ID's.

Description

Preprocess subjects ID's.

Usage

preprocessIds(dataset)

Arguments

Value

list of consecutive ID's

Preprocess 'nocbvars' argument.

Description

Preprocess 'nocbvars' argument.

Usage

preprocessNocbvars(nocbvars)

Arguments

Pre-process outfun argument.

Description

Pre-process outfun argument.

Usage

preprocessOutfun(outfun)

Arguments

Value

an output function

Preprocess 'outvars' argument. 'Outvars' is a character vector which tells CAMPSIS the mandatory columns to keep in the output dataframe.

Description

Preprocess 'outvars' argument. 'Outvars' is a character vector which tells CAMPSIS the mandatory columns to keep in the output dataframe.

Usage

preprocessOutvars(outvars)

Arguments

Value

outvars

Preprocess 'replicates' argument.

Description

Preprocess 'replicates' argument.

Usage

preprocessReplicates(replicates, model)

Arguments

Value

number of replicates to simulate

Pre-process scenarios.

Description

Pre-process scenarios.

Usage

preprocessScenarios(scenarios)

Arguments

Preprocess the simulation settings.

Description

Preprocess the simulation settings.

Usage

preprocessSettings(settings, dest)

Arguments

Value

updated simulation settings

Preprocess 'slices' argument.

Description

Preprocess 'slices' argument.

Usage

preprocessSlices(slices, maxID)

Arguments

Value

slices if not NULL, otherwise total number of subjects

Preprocess TSLD and TDOS columns according to given dataset configuration.

Description

Preprocess TSLD and TDOS columns according to given dataset configuration.

Usage

preprocessTSLDAndTDOSColumn(table, config)

Arguments

Value

updated table

Pre-process tablefun argument.

Description

Pre-process tablefun argument.

Usage

preprocessTablefun(fun)

Arguments

Value

a function in any case

Process time-related columns according to given dataset configuration.

Description

Process time-related columns according to given dataset configuration.

Usage

processAllTimeColumns(table, config)

Arguments

Value

updated table

Process arm labels. Arm identifiers in ARM column are replaced by arm labels as soon as one arm label is provided.

Description

Process arm labels. Arm identifiers in ARM column are replaced by arm labels as soon as one arm label is provided.

Usage

processArmLabels(campsis, arms)

Arguments

Value

updated CAMPSIS output with arm labels instead of arm identifiers

Process 'DROP_OTHERS'.

Description

Process 'DROP_OTHERS'.

Usage

processDropOthers(x, outvars = character(0), dropOthers)

Arguments

Value

processed data frame

Preprocess arguments of the simulate method.

Description

Preprocess arguments of the simulate method.

Usage

processSimulateArguments(model, dataset, dest, outvars, dosing, settings)

Arguments

Value

a simulation configuration

Progress settings class.

Description

Progress settings class.

Slots

tick_slice

tick() is called after each simulated slice, default is TRUE. In some cases, when the number of subjects per slice is low, it may be useful disable this flag, to improve performance issues.

Protocol class.

Description

Protocol class.

Remove initial conditions.

Description

Remove initial conditions.

Usage

removeInitialConditions(model)

Arguments

Value

same model without initial conditions

Reorder output columns.

Description

Reorder output columns.

Usage

reorderColumns(results, dosing)

Arguments

Value

reordered dataframe

Repeat schedule.

Description

Repeat schedule.

Usage

repeatSchedule(x, schedule)

## S4 method for signature 'numeric,cyclic_schedule'
repeatSchedule(x, schedule)

## S4 method for signature 'numeric,repeat_at_schedule'
repeatSchedule(x, schedule)

## S4 method for signature 'numeric,undefined_schedule'
repeatSchedule(x, schedule)

Arguments

Value

resulting times vector

'Repeat at' schedule class.

Description

'Repeat at' schedule class.

Slots

times

times at which the event is repeated, numeric vector

Repeated schedule class. See this class as an interface.

Description

Repeated schedule class. See this class as an interface.

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

Description

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

Usage

retrieveParameterValue(model, paramName, default = NULL, mandatory = FALSE)

Arguments

Value

the standardized parameter value or the given default value if not found

RxODE/rxode2 engine class.

Description

RxODE/rxode2 engine class.

Slots

rxode2

logical field to indicate if CAMPSIS should use rxode2 (field set to TRUE) or RxODE (field set to FALSE). Default is TRUE.

Sample generic object.

Description

Sample generic object.

Usage

sample(object, n, ...)

## S4 method for signature 'constant_distribution,integer'
sample(object, n)

## S4 method for signature 'fixed_distribution,integer'
sample(object, n)

## S4 method for signature 'function_distribution,integer'
sample(object, n)

## S4 method for signature 'bootstrap_distribution,integer'
sample(object, n)

## S4 method for signature 'bolus,integer'
sample(object, n, ...)

## S4 method for signature 'infusion,integer'
sample(object, n, ...)

## S4 method for signature 'observations,integer'
sample(object, n, ...)

## S4 method for signature 'covariate,integer'
sample(object, n)

## S4 method for signature 'bootstrap,integer'
sample(object, n)

Arguments

Value

sampling result

Sample covariates list.

Description

Sample covariates list.

Usage

sampleCovariatesList(covariates, ids_within_arm, subset)

Arguments

Value

a dataframe of n rows, 1 column per covariate

Sample a distribution and return a tibble.

Description

Sample a distribution and return a tibble.

Usage

sampleDistributionAsTibble(distribution, n, colname)

Arguments

Value

a tibble of n rows and 1 column

Sample time-varying covariates.

Description

Sample time-varying covariates.

Usage

sampleTimeVaryingCovariates(object, armID, needsDV)

Arguments

Value

a data.frame

Scatter plot (or X vs Y plot).

Description

Scatter plot (or X vs Y plot).

Usage

scatterPlot(x, output, colour = NULL, time = NULL)

Arguments

Value

a ggplot object

Scenario class.

Description

Scenario class.

Slots

name

scenario name, single character string

model

either a CAMPSIS model, a function or lambda-style formula

dataset

either a CAMPSIS dataset, a function or lambda-style formula

Scenarios class.

Description

Scenarios class.

Convert seconds to hours.

Description

Convert seconds to hours.

Usage

seconds(x)

Arguments

Value

numeric vector in hours

Set the label.

Description

Set the label.

Usage

setLabel(object, x)

## S4 method for signature 'arm,character'
setLabel(object, x)

Arguments

Value

the updated object

Set the seed. The goal of this method is to centralize all calls to the R method 'set.seed'.

Description

Set the seed. The goal of this method is to centralize all calls to the R method 'set.seed'.

Usage

setSeed(seed)

Arguments

Set the number of subjects.

Description

Set the number of subjects.

Usage

setSubjects(object, x)

## S4 method for signature 'arm,integer'
setSubjects(object, x)

## S4 method for signature 'dataset,integer'
setSubjects(object, x)

Arguments

Value

the updated object

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)

Description

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)

Usage

setupPlanDefault(object)

Arguments

Value

nothing

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

Description

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

Usage

setupPlanSequential()

Value

nothing

Shaded plot (or prediction interval plot).

Description

Shaded plot (or prediction interval plot).

Usage

shadedPlot(
  x,
  output,
  colour = NULL,
  strat_extra = NULL,
  level = 0.9,
  alpha = 0.25
)

Arguments

Value

a ggplot object

Simulate function.

Description

Simulate function.

Usage

simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'replicated_campsis_model,
##   dataset,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   dataset,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   data.frame,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   rxode_engine,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   mrgsolve_engine,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

Arguments

Value

dataframe with all results

Simulation delegate (several replicates).

Description

Simulation delegate (several replicates).

Usage

simulateDelegate(
  model,
  dataset,
  dest,
  events,
  scenarios,
  tablefun,
  outvars,
  outfun,
  seed,
  replicates,
  dosing,
  settings
)

Arguments

Value

a data frame with the results

Simulation delegate core (single replicate).

Description

Simulation delegate core (single replicate).

Usage

simulateDelegateCore(
  model,
  dataset,
  dest,
  events,
  tablefun,
  outvars,
  outfun,
  seed,
  replicates,
  dosing,
  settings
)

Arguments

Value

a data frame with the results

Simulation scenarios.

Description

Simulation scenarios.

Usage

simulateScenarios(
  scenarios,
  model,
  dataset,
  dest,
  events,
  tablefun,
  outvars,
  outfun,
  seed,
  replicates,
  dosing,
  settings
)

Arguments

Value

a data frame with the results

Simulation engine class.

Description

Simulation engine class.

Simulation progress class.

Description

Simulation progress class.

Arguments

Simulation settings class.

Description

Simulation settings class.

Slots

hardware

hardware settings object

solver

solver settings object

nocb

NOCB settings object

declare

declare settings (mrgsolve only)

progress

progress settings

replication

replication settings

internal

internal settings

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

Description

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

Slots

atol

absolute solver tolerance, default is 1e-08

rtol

relative solver tolerance, default is 1e-08

hmax

limit how big a solver step can be, default is NA

maxsteps

max steps between 2 integration times (e.g. when observations records are far apart), default is 70000

method

solver method, for RxODE/rxode2 only: 'liblsoda' (default), 'lsoda', 'dop853', 'indLin'. Mrgsolve's method is always 'lsoda'.

Spaghetti plot.

Description

Spaghetti plot.

Usage

spaghettiPlot(x, output, colour = NULL)

Arguments

Value

plot

Split dataset according to config.

Description

Split dataset according to config.

Usage

splitDataset(dataset, config)

Arguments

Value

a subset of the given dataset

Standardise time to hours.

Description

Standardise time to hours.

Usage

standardiseTime(x, unit)

Arguments

Value

numeric vector with the times converted to hours

Time-varying covariate class.

Description

Time-varying covariate class.

Convert dataset to dataset summary (internal method).

Description

Convert dataset to dataset summary (internal method).

Usage

toDatasetSummary(dataset)

Value

a dataset summary

Convert user-given distribution to an explicit CAMPSIS distribution. Passed distribution can be: - a NULL value. In that case, it will be converted into an 'UndefinedDistribution'. - a single numeric value. In that case, it will be converted into a 'ConstantDistribution'. - a numeric vector. In that case, it will be converted into a 'FixedDistribution'. - all available types of distribution. In this case, no conversion is applied.

Description

Convert user-given distribution to an explicit CAMPSIS distribution. Passed distribution can be: - a NULL value. In that case, it will be converted into an 'UndefinedDistribution'. - a single numeric value. In that case, it will be converted into a 'ConstantDistribution'. - a numeric vector. In that case, it will be converted into a 'FixedDistribution'. - all available types of distribution. In this case, no conversion is applied.

Usage

toExplicitDistribution(distribution)

Arguments

Value

a distribution object

Treatment class.

Description

Treatment class.

Treatment IOV class.

Description

Treatment IOV class.

Slots

colname

name of the column that will be output in dataset

distribution

distribution

dose_numbers

associated dose numbers, integer vector, same length as values

Treatment IOV's class.

Description

Treatment IOV's 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.

Description

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 schedule class.

Description

Undefined schedule class.

Unite the given column names.

Description

Unite the given column names.

Usage

uniteColumns(x, columns, colname, factor = TRUE)

Arguments

Value

a data frame

Unwrap treatment.

Description

Unwrap treatment.

Usage

unwrapTreatment(object)

## S4 method for signature 'bolus'
unwrapTreatment(object)

## S4 method for signature 'infusion'
unwrapTreatment(object)

## S4 method for signature 'bolus_wrapper'
unwrapTreatment(object)

## S4 method for signature 'infusion_wrapper'
unwrapTreatment(object)

## S4 method for signature 'treatment'
unwrapTreatment(object)

## S4 method for signature 'arm'
unwrapTreatment(object)

## S4 method for signature 'arms'
unwrapTreatment(object)

## S4 method for signature 'dataset'
unwrapTreatment(object)

Arguments

Value

updated object

Update the number of additional doses (ADDL).

Description

Update the number of additional doses (ADDL).

Usage

updateADDL(object, addl, ref = NULL)

## S4 method for signature 'bolus_wrapper,integer,character'
updateADDL(object, addl, ref = NULL)

## S4 method for signature 'infusion_wrapper,integer,character'
updateADDL(object, addl, ref = NULL)

## S4 method for signature 'bolus,integer,character'
updateADDL(object, addl, ref = NULL)

## S4 method for signature 'infusion,integer,character'
updateADDL(object, addl, ref = NULL)

## S4 method for signature 'treatment,integer,character'
updateADDL(object, addl, ref = NULL)

## S4 method for signature 'arm,integer,character'
updateADDL(object, addl, ref = NULL)

## S4 method for signature 'arms,integer,character'
updateADDL(object, addl, ref = NULL)

## S4 method for signature 'dataset,integer,character'
updateADDL(object, addl, ref = NULL)

Arguments

Value

updated object

Update amount.

Description

Update amount.

Usage

updateAmount(object, amount, ref)

## S4 method for signature 'bolus,numeric,character'
updateAmount(object, amount, ref)

## S4 method for signature 'infusion,numeric,character'
updateAmount(object, amount, ref)

## S4 method for signature 'bolus_wrapper,numeric,character'
updateAmount(object, amount, ref)

## S4 method for signature 'infusion_wrapper,numeric,character'
updateAmount(object, amount, ref)

## S4 method for signature 'treatment,numeric,character'
updateAmount(object, amount, ref)

## S4 method for signature 'arm,numeric,character'
updateAmount(object, amount, ref)

## S4 method for signature 'arms,numeric,character'
updateAmount(object, amount, ref)

## S4 method for signature 'dataset,numeric,character'
updateAmount(object, amount, ref)

Arguments

Value

updated object

Update the inter-dose interval (II).

Description

Update the inter-dose interval (II).

Usage

updateII(object, ii, ref = NULL)

## S4 method for signature 'bolus_wrapper,numeric,character'
updateII(object, ii, ref = NULL)

## S4 method for signature 'infusion_wrapper,numeric,character'
updateII(object, ii, ref = NULL)

## S4 method for signature 'bolus,numeric,character'
updateII(object, ii, ref = NULL)

## S4 method for signature 'infusion,numeric,character'
updateII(object, ii, ref = NULL)

## S4 method for signature 'treatment,numeric,character'
updateII(object, ii, ref = NULL)

## S4 method for signature 'arm,numeric,character'
updateII(object, ii, ref = NULL)

## S4 method for signature 'arms,numeric,character'
updateII(object, ii, ref = NULL)

## S4 method for signature 'dataset,numeric,character'
updateII(object, ii, ref = NULL)

Arguments

Value

updated object

Update the repeat field (argument 'rep' in Bolus and Infusion constructors).

Description

Update the repeat field (argument 'rep' in Bolus and Infusion constructors).

Usage

updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'bolus_wrapper,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'infusion_wrapper,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'bolus,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'infusion,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'treatment,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'arm,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'arms,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

## S4 method for signature 'dataset,repeated_schedule,character'
updateRepeat(object, rep, ref = NULL)

Arguments

Value

updated object

VPC plot.

Description

VPC plot.

Usage

vpcPlot(x, scenarios = NULL, level = 0.9, alpha = 0.15)

Arguments

Value

a ggplot object

Convert weeks to hours.

Description

Convert weeks to hours.

Usage

weeks(x)

Arguments

Value

numeric vector in hours

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

Description

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

Usage

years(x)

Arguments

Value

numeric vector in hours