Package 'campsisnca'

Title: Non-Compartmental Analysis for Campsis Simulation Platform
Description: A flexible and user-friendly non-compartmental analysis (NCA) toolkit designed to work seamlessly with simulated pharmacokinetic data generated using the 'campsis' ecosystem. The package provides a comprehensive framework to compute standard and custom NCA metrics, including exposure (AUC), peak/trough concentrations, half-life and time-above/below thresholds, with support for configurable time windows and summary statistics. 'campsisnca' integrates tightly with 'campsis' and 'campsismod', enabling streamlined workflows from simulation to analysis. In addition, the package provides a JSON-based interface to define NCA analyses, metrics and options using formal schemas, allowing analyses to be created, validated and executed outside of R and facilitating reproducibility, automation and system integration. The package also includes utilities for generating formatted summary tables and exporting results in multiple formats suitable for reporting. Trapezoidal rule implementation for AUC calculation is based on the 'qpNCA' package by Huisman, Jolling, Mehta and Bergsma (2021) <doi:10.32614/CRAN.package.qpNCA>, following methodology from Rowland and Tozer (2011, ISBN:978-0-683-07404-8). The package itself is licensed under the GPL (>= 3); the JSON schema files shipped in inst/extdata are licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0).
Authors: Nicolas Luyckx [aut, cre], Calvagone [cph]
Maintainer: Nicolas Luyckx <[email protected]>
License: GPL (>= 3)
Version: 1.7.2
Built: 2026-09-30 20:28:19 UTC
Source: https://github.com/calvagone/campsisnca

Help Index

Abstract avg metric class.

Description

Abstract avg metric class.

Abstract last metric class.

Description

Abstract last metric class.

Abstract max metric class.

Description

Abstract max metric class.

Abstract min metric class.

Description

Abstract min metric class.

Abstract time above/below limit class.

Description

Abstract time above/below limit class.

Abstract value at metric class.

Description

Abstract value at metric class.

Apply time window.

Description

Apply time window.

Usage

apply_time_window(x, window, data_time_unit, ...)

## S4 method for signature 'ANY,nca_time_window,character'
apply_time_window(x, window, data_time_unit)

Arguments

x

input data for the calculation, data frame
window

time window
data_time_unit

time unit of TIME column in data (x argument)
...

extra arguments

Value

updated object

Apply time window.

Description

'r lifecycle::badge("deprecated")'

Usage

applyTimeWindow(x, window, data_time_unit, ...)

Arguments

x

input data for the calculation, data frame
window

time window
data_time_unit

time unit of TIME column in data (x argument)
...

extra arguments

Details

'applyTimeWindow()' is deprecated in favor of 'apply_time_window()'.

Value

updated object

AUC.

Description

AUC.

Usage

AUC(
  variable = NULL,
  window = NULL,
  method = 1,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
method

method: * 1: linear up - linear down * 2: linear up - logarithmic down * 3: linear before Tmax, logarithmic after Tmax
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Details

Trapezoidal rule implementation for AUC calculation is based on the 'qpNCA' package by Huisman, Jolling, Mehta and Bergsma (2021) <doi:10.32614/CRAN.package.qpNCA>, following methodology from Rowland and Tozer (2011, ISBN:978-0-683-07404-8).

Value

an object of class auc_metric

AUC metric class.

Description

AUC metric class.

Avg.

Description

Avg.

Usage

Avg(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class avg_metric

Avg metric class.

Description

Avg metric class.

Calculate.

Description

Calculate.

Usage

calculate(object, x, options = NULL, ...)

## S4 method for signature 'nca_metric,ANY'
calculate(object, x, options = NULL, ...)

## S4 method for signature 'theoretical_thalf_metric,ANY'
calculate(object, x, options = NULL, ...)

## S4 method for signature 'nca_analysis,ANY'
calculate(object, x, options = NULL, ...)

## S4 method for signature 'nca_analyses,ANY'
calculate(object, x, options = NULL, ...)

## S4 method for signature 'nca_table,ANY'
calculate(object, x, options = NULL, ...)

## S4 method for signature 'replicated_nca_table,campsis_output'
calculate(object, x, options = NULL, ...)

Arguments

object

object (NCA table, NCA analyses, NCA analysis, PK metric) where calculation is applied
x

input data for the calculation, data frame
options

NCA options
...

extra arguments

Value

updated object

The Campsis Output Class Union

Description

A class union containing standard data frames, tibbles, and Campsis-specific table objects.

The Campsisnca Output Class Union

Description

A class union containing standard data frames, tibbles, and Campsisnca-specific table objects.

Campsisnca table class (see this class as an interface)

Description

Campsisnca table class (see this class as an interface)

CAt (concentration at specific time).

Description

CAt (concentration at specific time).

Usage

CAt(
  variable = NULL,
  window = NULL,
  time = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
time

what time to read the concentrations. If not provided, last concentrations from x will be returned.
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class conc_at_metric

Cavg.

Description

Cavg.

Usage

Cavg(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class cavg_metric

Cavg metric class.

Description

Cavg metric class.

Alias for Change from Baseline (CFB).

Description

Alias for Change from Baseline (CFB).

Usage

CFB(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL,
  method = "difference"
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README
method

Character string specifying the calculation method. Must be one of "difference" (default), "percent", "ratio", or "log".

Value

an object of class cfb_metric

Change from Baseline metric class.

Description

Change from Baseline metric class.

Slots

method

Character string specifying the CFB method ("difference", "percent", "ratio", "log").

Change from Baseline (CFB).

Description

Change from Baseline (CFB).

Usage

ChangeFromBaseline(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL,
  method = "difference"
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README
method

Character string specifying the calculation method. Must be one of "difference" (default), "percent", "ratio", or "log".

Value

an object of class cfb_metric

Cmax.

Description

Cmax.

Usage

Cmax(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class cmax_metric

Cmax metric class.

Description

Cmax metric class.

Cmin

Description

Cmin

Usage

Cmin(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class cmin_metric

Cmin metric class.

Description

Cmin metric class.

Compute NCA metric summary.

Description

Compute NCA metric summary.

Usage

compute_nca_metric_summary(object, strat_vars, quantile_type)

Arguments

object

NCA metric
strat_vars

stratification variable names in data
quantile_type

type of quantile

Value

data frame

Compute the duration of a line segment above or below a certain limit.

Description

Compute the duration of a line segment above or below a certain limit.

Usage

computeTimeAboveBelow(x1, y1, x2, y2, above, strictly)

Arguments

x1

x-coordinate of the first point
y1

y-coordinate of the first point
x2

x-coordinate of the second point
y2

y-coordinate of the second point
above

whether the line segment is above the limit
strictly

whether the line segment is strictly above or below the limit

Value

the duration of the line segment above or below the limit

Concentration at metric class.

Description

Concentration at metric class.

Ctrough. Last time in x shall be considered as the trough time. Similar to Last, but for concentrations.

Description

Ctrough. Last time in x shall be considered as the trough time. Similar to Last, but for concentrations.

Usage

Ctrough(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class ctrough_metric

Ctrough metric class.

Description

Ctrough metric class.

Custom metric class.

Description

Custom metric class.

Custom metric (input data as time and value vectors).

Description

Custom metric (input data as time and value vectors).

Usage

CustomMetric(
  variable = NULL,
  window = NULL,
  fun,
  name = NULL,
  unit = NULL,
  categorical = FALSE,
  stat_display = get_stat_display_default(categorical),
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
fun

any custom function with exactly 2 arguments: time and value
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
categorical

categorical endpoint, logical
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class custom_metric

Custom metric (input data as tibble).

Description

Custom metric (input data as tibble).

Usage

CustomMetricTbl(
  window = NULL,
  fun,
  name = NULL,
  unit = NULL,
  categorical = FALSE,
  stat_display = get_stat_display_default(categorical),
  digits = NULL
)

Arguments

window

time window on which this metric should be computed
fun

any custom function with exactly 1 argument: data
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
categorical

categorical endpoint, logical
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class custom_metric

Compute the coefficient of variation.

Description

Compute the coefficient of variation.

Usage

cv(x)

Arguments

x

numeric vector

Value

numeric value

Dataframe export type class.

Description

Dataframe export type class.

Custom deparse function. Works similarly to deparse1. However, lines are trimmed before being concatenated.

Description

Custom deparse function. Works similarly to deparse1. However, lines are trimmed before being concatenated.

Usage

deparse_digits(digits)

Arguments

digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these)

Value

a character vector, which will be pasted and given to gtsummary

Deparse 1 line.

Description

Deparse 1 line.

Usage

deparse_one_line(x)

Arguments

x

expression to deparse

Value

a single string

Discard category column.

Description

Discard category column.

Usage

discard_category_column(x, split = "_")

Arguments

x

summary export
split

string use to concatenate the 'stat' and 'category' column

Value

updated data frame

Discard category column.

Description

'r lifecycle::badge("deprecated")'

Usage

discardCategoryColumn(x, split = "_")

Arguments

x

summary export
split

string use to concatenate the 'stat' and 'category' column

Details

'discardCategoryColumn()' is deprecated in favor of 'discard_category_column()'.

Value

updated object

Export replicated NCA table.

Description

Export replicated NCA table.

Usage

## S4 method for signature 'replicated_nca_table,character'
export(object, dest, ...)

Arguments

object

replicated_nca_table object
dest

destination for the summarised table, either "dataframe", "gtsummary" or "gt"
...

extra arguments

Value

a data frame, a gtsummary object or a gt object, depending on the destination

Extract values from within braces.

Description

Extract values from within braces.

Usage

extract_brace_values(x)

Arguments

x

input string

Value

a character vector

Generate table code.

Description

Generate table code.

Usage

generate_table_code(object, init = NULL, ...)

## S4 method for signature 'nca_table,logical'
generate_table_code(object, init = NULL, ...)

Arguments

object

table object
init

generate initialization code to generate the individuals, default is TRUE
...

extra arguments

Value

a character vector containing the code

Compute the geometric CV.

Description

Compute the geometric CV.

Usage

geocv(x)

Arguments

x

numeric vector

Value

numeric value

Compute the geometric mean.

Description

Compute the geometric mean.

Usage

geomean(x)

Arguments

x

numeric vector

Value

numeric value

Filter Column Names to Character (and Optionally Factor) Columns

Description

Filters a vector of column names to include only those present in a data frame that are of class character (and optionally factor).

Usage

get_character_cols_only(x, cols, include_factor = FALSE)

Arguments

x

A data frame or tibble.
cols

A character vector of candidate column names.
include_factor

Logical. If TRUE, factor columns are also included along with character columns. Defaults to FALSE.

Value

A character vector of column names matching the specified class criteria.

Get default name.

Description

Get default name.

Usage

get_default_name(object, ...)

## S4 method for signature 'nca_metric'
get_default_name(object, ...)

## S4 method for signature 'auc_metric'
get_default_name(object, ...)

## S4 method for signature 'max_metric'
get_default_name(object, ...)

## S4 method for signature 'cmax_metric'
get_default_name(object, ...)

## S4 method for signature 'tmax_metric'
get_default_name(object, ...)

## S4 method for signature 'min_metric'
get_default_name(object, ...)

## S4 method for signature 'cmin_metric'
get_default_name(object, ...)

## S4 method for signature 'tmin_metric'
get_default_name(object, ...)

## S4 method for signature 'value_at_metric'
get_default_name(object, ...)

## S4 method for signature 'conc_at_metric'
get_default_name(object, ...)

## S4 method for signature 'last_metric'
get_default_name(object, ...)

## S4 method for signature 'ctrough_metric'
get_default_name(object, ...)

## S4 method for signature 'avg_metric'
get_default_name(object, ...)

## S4 method for signature 'cavg_metric'
get_default_name(object, ...)

## S4 method for signature 'thalf_metric'
get_default_name(object, ...)

## S4 method for signature 'theoretical_thalf_metric'
get_default_name(object, ...)

## S4 method for signature 'time_above_limit_metric'
get_default_name(object, ...)

## S4 method for signature 'time_below_limit_metric'
get_default_name(object, ...)

## S4 method for signature 'cfb_metric'
get_default_name(object, ...)

Arguments

object

get default name of this object
...

optional extra arguments

Value

a character string (the default name)

Get digits code for gtsummary.

Description

Get digits code for gtsummary.

Usage

get_digits_code(table)

Arguments

table

NCA table

Value

code

Get labels code for gtsummary.

Description

Get labels code for gtsummary.

Usage

get_labels_code(table, subscripts)

Arguments

table

NCA table
subscripts

use subscripts, logical value

Value

code

Get the name of the metric in LaTeX notation (with subscript coded with an underscore and brackets).

Description

Get the name of the metric in LaTeX notation (with subscript coded with an underscore and brackets).

Usage

get_latex_name(x, ...)

## S4 method for signature 'nca_metric'
get_latex_name(x)

## S4 method for signature 'abstract_max_metric'
get_latex_name(x)

## S4 method for signature 'tmax_metric'
get_latex_name(x)

## S4 method for signature 'abstract_min_metric'
get_latex_name(x)

## S4 method for signature 'tmin_metric'
get_latex_name(x)

## S4 method for signature 'abstract_value_at_metric'
get_latex_name(x)

## S4 method for signature 'last_metric'
get_latex_name(x)

## S4 method for signature 'ctrough_metric'
get_latex_name(x)

## S4 method for signature 'abstract_avg_metric'
get_latex_name(x)

## S4 method for signature 'thalf_metric'
get_latex_name(x)

## S4 method for signature 'theoretical_thalf_metric'
get_latex_name(x)

## S4 method for signature 'cfb_metric'
get_latex_name(x)

Arguments

x

metric
...

extra arguments, not used

Value

a character string

Get statistics code for gtsummary.

Description

Get statistics code for gtsummary.

Usage

get_statistics_code(table)

Arguments

table

NCA table

Value

code

Get all stratas.

Description

Get all stratas.

Usage

get_strata(object, keep_single = NULL, ...)

## S4 method for signature 'nca_table,logical'
get_strata(object, keep_single = NULL, ...)

Arguments

object

table object
keep_single

keep single stratification values, logical (default TRUE)
...

extra arguments

Value

list of stratification variable names

Get table summary code.

Description

Get table summary code.

Usage

get_table_summary_code(
  variable,
  data,
  by,
  stats,
  type,
  labels,
  digits,
  combine_with,
  header_label
)

Arguments

variable

assigned variable name
data

data frame code
by

variable
stats

stats to compute
type

type of the variables
labels

the labels to display
digits

the digits to be used for rounding
combine_with

either 'tbl_stack' or 'tbl_merge'
header_label

header label name

Value

data frame

Get the unit corresponding to the given metric.

Description

Get the unit corresponding to the given metric.

Usage

get_unit(object, metric, ...)

## S4 method for signature 'nca_metrics,character'
get_unit(object, metric, ...)

## S4 method for signature 'nca_analysis,character'
get_unit(object, metric, ...)

## S4 method for signature 'nca_table,character'
get_unit(object, metric, ...)

Arguments

object

any object that contains units
metric

given metric name
...

extra arguments, not used

Value

a character string (the unit)

Get the variable type code for gtsummary.

Description

Get the variable type code for gtsummary.

Usage

get_variable_type_code(table, all_dichotomous_levels)

Arguments

table

NCA table
all_dichotomous_levels

show all dichotomous levels (0 and 1) when data is dichotomous

Value

code

Get Campsisnca option logic.

Description

Get Campsisnca option logic.

Usage

getCampsisncaOption(name, default)

Arguments

name

option to search
default

default value if option not found

Value

option value

Glue stat display string.

Description

Glue stat display string.

Usage

glue_stat_display(stat_display, stats, summary, digits)

Arguments

stat_display

stat display string
stats

statistics, character vector
summary

summary data frame
digits

digits to be used for rounding

Value

glued string

Gt export type class.

Description

Gt export type class.

Gt summary export type class.

Description

Gt summary export type class.

Compute the individual value of an individual.

Description

Compute the individual value of an individual.

Usage

i_value(object, time, value = NULL)

## S4 method for signature 'auc_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'abstract_max_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'tmax_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'abstract_min_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'tmin_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'abstract_value_at_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'abstract_last_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'abstract_avg_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'thalf_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 
## 'abstract_time_above_or_below_limit_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'cfb_metric,numeric,numeric'
i_value(object, time, value = NULL)

## S4 method for signature 'custom_metric,numeric,numeric'
i_value(object, time, value = NULL)

Arguments

object

PK metric
time

time vector, numeric
value

value vector, numeric

Value

individual value

Compute the individual value of an individual.

Description

Compute the individual value of an individual.

Usage

i_value_tbl(object, data, ...)

## S4 method for signature 'custom_metric,tbl_df'
i_value_tbl(object, data)

Arguments

object

PK metric
data

individual data, tibble
...

extra arguments

Value

individual value

Compute the individual values on a simulation output.

Description

Compute the individual values on a simulation output.

Usage

i_values(object, x, options, strat_vars, ...)

## S4 method for signature 'nca_metric'
i_values(object, x, options, strat_vars, ...)

Arguments

object

PK metric
x

input data for the calculation, data frame
options

NCA options
strat_vars

stratification variable names
...

extra arguments

Value

individual values

Individual Campsisnca table class

Description

Individual Campsisnca table class

Individual (wide format) Campsisnca table class

Description

Individual (wide format) Campsisnca table class

Compute the individual value of an individual.

Description

'r lifecycle::badge("deprecated")'

Usage

iValue(object, time, value = NULL)

Arguments

object

PK metric
time

time vector, numeric
value

value vector, numeric

Details

'iValue()' is deprecated in favor of 'i_value()'.

Value

individual value

Last value.

Description

Last value.

Usage

Last(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class last_metric

Last metric class.

Description

Last metric class.

Max.

Description

Max.

Usage

Max(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class max_metric

Max metric class.

Description

Max metric class.

Theoretical metrics for 1-cpt model results.

Description

Theoretical metrics for 1-cpt model results.

Usage

metrics.1cpt(x, map = character(0))

Arguments

x

CAMPSIS/NONMEM dataframe
map

character vector used for column mapping, only one key is possible: K

Value

theoretical metrics

Theoretical metrics for 2-cpt model results.

Description

Theoretical metrics for 2-cpt model results.

Usage

metrics.2cpt(x, map = character(0))

Arguments

x

CAMPSIS/NONMEM dataframe
map

character vector used for column mapping, keys to be chosen among: DOSE, TAU, CL, V2, Q, V3, KA

Value

theoretical metrics

Pre-processing for metrics.1cpt and metrics.2cpt.

Description

Pre-processing for metrics.1cpt and metrics.2cpt.

Usage

metrics.common(x, map, thalf.1cpt)

Arguments

x

CAMPSIS/NONMEM dataframe
map

character vector used for column mapping, only one key is possible: K
thalf.1cpt

logical value

Value

theoretical metrics

Min.

Description

Min.

Usage

Min(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class min_metric

Min metric class.

Description

Min metric class.

NCA analyses class.

Description

NCA analyses class.

NCA analysis class.

Description

NCA analysis class.

NCA metric class. See this class as abstract class.

Description

NCA metric class. See this class as abstract class.

NCA metrics class. See this class as a list of NCA metrics.

Description

NCA metrics class. See this class as a list of NCA metrics.

NCA options class.

Description

NCA options class.

NCA table class.

Description

NCA table class.

NCA time window class.

Description

NCA time window class.

Create an NCA analysis.

Description

Create an NCA analysis.

Usage

NCAAnalysis(
  name = "Default",
  window = TimeWindow(),
  variable = NULL,
  strata = get_default_strata()
)

Arguments

name

name of this analysis, e.g. 'Day 1'
window

time window, see TimeWindow
variable

default variable which is analysed
strata

strata levels this analysis refers to, named vector, e.g. c(ARM='1g QD'). Note, the default strata are c(SCENARIO='all', ARM='all'). Use 'all' if this analysis refers to all levels for the specified stratification variable. By default, a stratification variable that has only 1 level is ignored.

Value

an object of class nca_analysis

NCA metrics

Description

NCA metrics

Usage

NCAMetrics()

Value

an object of class nca_metrics

NCA table (deprecated).

Description

NCA table (deprecated).

Usage

NCAMetricsTable(
  title = NULL,
  subtitle = NULL,
  swap_strat = FALSE,
  combine_with = "tbl_stack",
  show_all_levels = FALSE,
  header_label = "Metric",
  subscripts = TRUE,
  nca_options = NCAOptions(),
  tab_options = list(),
  json = NULL
)

Arguments

title

table title, optional character value
subtitle

table subtitle, optional character value
swap_strat

swap stratification variables in table (only useful when 2 stratification variables are given)
combine_with

either 'tbl_stack' or 'tbl_merge'
show_all_levels

show all dichotomous levels in table
header_label

'Metric' by default
subscripts

use LaTeX subcripts/superscripts notation when writing labels
nca_options

NCA options, see ?NCAOptions
tab_options

list of options to pass to gt::tab_options
json

path to JSON table file or JSON content in string form

Value

an object of class nca_table

NCA options used for calculation of metrics.

Description

NCA options used for calculation of metrics.

Usage

NCAOptions(
  quantile_type = 2L,
  data_time_unit = "hour",
  table_time_unit = "hour"
)

Arguments

quantile_type

type of quantile to use (see ?quantile), default value in campsisnca is 2 (aligned with gtsummary)
data_time_unit

time unit of the data given to 'calculate'
table_time_unit

time unit in table (for time-dependent metrics like AUC, Time above and below, etc.)

Value

an object of class nca_options

NCA table.

Description

NCA table.

Usage

NCATable(
  title = NULL,
  subtitle = NULL,
  swap_strat = FALSE,
  combine_with = "tbl_stack",
  show_all_levels = FALSE,
  header_label = "Metric",
  subscripts = TRUE,
  nca_options = NCAOptions(),
  tab_options = list(),
  json = NULL
)

Arguments

title

table title, optional character value
subtitle

table subtitle, optional character value
swap_strat

swap stratification variables in table (only useful when 2 stratification variables are given)
combine_with

either 'tbl_stack' or 'tbl_merge'
show_all_levels

show all dichotomous levels in table
header_label

'Metric' by default
subscripts

use LaTeX subcripts/superscripts notation when writing labels
nca_options

NCA options, see ?NCAOptions
tab_options

list of options to pass to gt::tab_options
json

path to JSON table file or JSON content in string form

Value

an object of class nca_table

Simulated Pharmacokinetic Dataset (Multiple-Dose Bolus)

Description

A simulated 2-compartment oral dataset containing 200 subjects administered 1000 mg bolus doses every 24 hours for 7 days. Includes rich sampling on Day 1 and Day 7, sparse sampling on intermediate days, and weight-based allometric scaling on clearance.

Usage

pk_bolus_md

Format

A tibble with 5,000 rows and 16 variables:

ID

Subject identifier (1–200)

TIME

Time after initial dose (hours)

ARM

Study arm identifier

A_DEPOT

Amount in the depot (absorption) compartment (mg)

A_CENTRAL

Amount in the central compartment (mg)

A_PERIPHERAL

Amount in the peripheral compartment (mg)

A_OUTPUT

Eliminated amount (mg)

BW

Body weight covariate (kg), sampled from Uniform(50, 100)

CL

Individual clearance (L/h), allometrically scaled with body weight

V2

Central volume of distribution (L)

Q

Inter-compartmental clearance (L/h)

V3

Peripheral volume of distribution (L)

KA

Absorption rate constant (1/h)

CP

True plasma concentration in central compartment (mg/L or mcg/mL)

OBS_CP

Observed plasma concentration with ~15.8% proportional residual variability

Y

Observation variable (identical to OBS_CP)

Source

Simulated using the campsis package (ADVAN4/TRANS4 model).

Examples

data(pk_bolus_md)
head(pk_bolus_md)

Preserve Existing Column Value Order as Factor Levels

Description

Converts target columns into factors using their current unique row appearance order as the factor levels.

Usage

preserve_column_levels(x, cols)

Arguments

x

a data frame or tibble.
cols

a character vector of column names to convert.

Value

a data frame with updated factor columns.

Strip Factor Class from Columns

Description

Converts target columns from factors into standard character vectors.

Usage

remove_column_levels(x, cols)

Arguments

x

a data frame or tibble.
cols

a character vector of column names to convert.

Value

a data frame with character columns.

Replicated NCA table class.

Description

Replicated NCA table class.

Replicated NCA table.

Description

Replicated NCA table.

Usage

ReplicatedNCATable(
  title = NULL,
  subtitle = NULL,
  selected_statistics = character(),
  summary_stat_display = get_stat_display_default(),
  summary_stat_signif_digits = 3L,
  strata = get_default_strata(),
  tab_options = list(),
  json = NULL
)

Arguments

title

table title, optional character value
subtitle

table subtitle, optional character value
selected_statistics

NCA metrics statistics to keep (e.g. mean, etc) when summary statistics are computed on replicated output. Default is the empty character vector (all statistics are computed).
summary_stat_display

display format for replicate statistics, character vector. Default is ⁠'{median} ({p5}–{p95})'⁠.
summary_stat_signif_digits

number of significant digits to display for replicate statistics, default is 3.
strata

strata levels this analysis refers to, named vector, e.g. c(ARM='1g QD'). Note, the default strata are c(SCENARIO='all', ARM='all'). Use 'all' if this analysis refers to all levels for the specified stratification variable. By default, a stratification variable that has only 1 level is ignored.
tab_options

list of options to pass to gt::tab_options
json

path to JSON table file or JSON content in string form

Value

an object of class replicated_nca_table

Restore Factor Levels and Optionally Reorder Rows

Description

Re-applies saved factor levels back onto target columns, optionally physically sorts the rows by the restored factor order, and optionally converts the columns back to character vectors.

Usage

restore_column_levels(x, saved_levels, arrange = TRUE, to_character = FALSE)

Arguments

x

a data frame or tibble.
saved_levels

a named list of character vectors representing factor levels (typically created by save_column_levels).
arrange

logical. If TRUE (default), physically reorders rows in x to match the restored factor level sequence.
to_character

logical. If TRUE, converts the target columns back to character vectors after restoring levels and sorting. Defaults to FALSE.

Value

a data frame with restored factor (or character) columns and optionally reordered rows.

Extract and Save Column Factor Levels

Description

Extracts factor levels (or unique value order for non-factors) for target columns and stores them in a named list for later restoration.

Usage

save_column_levels(x, cols)

Arguments

x

a data frame or tibble.
cols

a character vector of column names whose levels should be saved.

Value

a named list where names correspond to column names and values contain character vectors of factor levels.

Compute the standard error.

Description

Compute the standard error.

Usage

se(x)

Arguments

x

numeric vector

Value

numeric value

Return the evaluated statistics display string. This method was kept for backward compatibility in the tests. It is recommended to call 'export(dest="dataframe", type="summary_pretty")' on the NCA table instead.

Description

Return the evaluated statistics display string. This method was kept for backward compatibility in the tests. It is recommended to call 'export(dest="dataframe", type="summary_pretty")' on the NCA table instead.

Usage

stat_display_string(object, ...)

## S4 method for signature 'nca_metric'
stat_display_string(object, ...)

Arguments

object

PK metric
...

extra arguments

Value

a string, e.g. 100 [45-143]

Summary Campsisnca table class

Description

Summary Campsisnca table class

Summary (pretty format) Campsisnca table class

Description

Summary (pretty format) Campsisnca table class

Summary (wide format) Campsisnca table class

Description

Summary (wide format) Campsisnca table class

Terminal half life computed by making a linear regression in the log domain on the given data x.

Description

Terminal half life computed by making a linear regression in the log domain on the given data x.

Usage

Thalf(
  variable = NULL,
  window = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class thalf_metric

Thalf metric class.

Description

Thalf metric class.

Theoretical half life for a 1-compartment model.

Description

Theoretical half life for a 1-compartment model.

Usage

Thalf.1cpt(
  map = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

map

character vector used for column mapping, only one key is possible: K
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class theoretical_thalf_metric

Thalf metric required columns for a 1-compartment model.

Description

Thalf metric required columns for a 1-compartment model.

Usage

thalf.1cpt.required()

Value

character vector of required column names

Theoretical distribution half life for a 2-compartment model.

Description

Theoretical distribution half life for a 2-compartment model.

Usage

Thalf.2cpt.dist(
  map = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

map

character vector used for column mapping, keys to be chosen among: DOSE, TAU, CL, V2, Q, V3, KA
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class theoretical_thalf_metric

Theoretical effective half life for a 2-compartment model.

Description

Theoretical effective half life for a 2-compartment model.

Usage

Thalf.2cpt.eff(
  map = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

map

character vector used for column mapping, keys to be chosen among: DOSE, TAU, CL, V2, Q, V3, KA
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class theoretical_thalf_metric

Thalf metric required columns for a 2-compartment model.

Description

Thalf metric required columns for a 2-compartment model.

Usage

thalf.2cpt.required()

Value

character vector of required column names

Theoretical elimination half life for a 2-compartment model.

Description

Theoretical elimination half life for a 2-compartment model.

Usage

Thalf.2cpt.z(
  map = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

map

character vector used for column mapping, keys to be chosen among: DOSE, TAU, CL, V2, Q, V3, KA
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class theoretical_thalf_metric

Theoretical thalf metric class.

Description

Theoretical thalf metric class.

Time above limit metric class.

Description

Time above limit metric class.

Time below limit metric class.

Description

Time below limit metric class.

Time above a certain limit.

Description

Time above a certain limit.

Usage

TimeAboveLimit(
  variable = NULL,
  window = NULL,
  limit = NULL,
  strictly = FALSE,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
limit

the limit to compare the variable against
strictly

whether the variable must be strictly above the limit
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class time_above_limit_metric

Time below a certain limit.

Description

Time below a certain limit.

Usage

TimeBelowLimit(
  variable = NULL,
  window = NULL,
  limit = NULL,
  strictly = FALSE,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
limit

the limit to compare the variable against
strictly

whether the variable must be strictly below the limit
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class time_below_limit_metric

Filter CAMPSIS dataset based on min and max time.

Description

Filter CAMPSIS dataset based on min and max time.

Usage

timerange(
  x,
  min = 0,
  max = Inf,
  exclmin = FALSE,
  exclmax = FALSE,
  rebase = FALSE
)

Arguments

x

CAMPSIS/NONMEM dataframe
min

min time
max

max time
exclmin

exclude min time when filtering
exclmax

exclude max time when filtering
rebase

rebase first time to origin, logical value, FALSE by default

Value

dataset subset

Create a time window object.

Description

Create a time window object.

Usage

TimeWindow(
  start = 0,
  end = "last",
  time_unit = "hour",
  exclude_start = FALSE,
  exclude_end = FALSE
)

Arguments

start

start time of window
end

end time of window, use 'last' to specify the end of the simulation output
time_unit

time unit of 'start' and 'end'
exclude_start

exclude start time when filtering
exclude_end

exclude end time when filtering

Value

a time range object

Tmax.

Description

Tmax.

Usage

Tmax(
  variable = NULL,
  window = NULL,
  rebase = TRUE,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
rebase

rebase time according to start time of window
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class tmax_metric

Tmax metric class.

Description

Tmax metric class.

Tmin.

Description

Tmin.

Usage

Tmin(
  variable = NULL,
  window = NULL,
  rebase = TRUE,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
rebase

rebase time according to start time of window
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class tmin_metric

Tmin metric class.

Description

Tmin metric class.

Gtsummary to Gt.

Description

Gtsummary to Gt.

Usage

toGt(
  x,
  title = NULL,
  subtitle = NULL,
  opts = list(),
  subscripts = FALSE,
  fmt_markdown = FALSE
)

Arguments

x

gtsummary table
title

table title
subtitle

table subtitle
opts

gt tab options
subscripts

use subscripts
fmt_markdown

transform any markdown-formatted text, logical value. Default is FALSE.

Value

a gt table object

NOTE: This method has been adapted from the 'qpNCA' package by Huisman, Jolling, Mehta and Bergsma (2021) <doi:10.32614/CRAN.package.qpNCA>, following methodology from Rowland and Tozer (2011, ISBN:978-0-683-07404-8).

Description

Calculate Area Under the Curve Using Trapezoids.

Usage

trap(x, y, method = 1)

Arguments

x

x variable, i.e. time
y

y variable, i.e. concentration
method

method: * 1: linear up - linear down * 2: linear up - logarithmic down * 3: linear before Tmax, logarithmic after Tmax

Details

Calculates AUC using the trapezoidal method. Assumes data represent a single profile. Despite choice of method, only linear interpolation is used for areas of intervals beginning or ending with y: 0.

Value

area (length-one numeric)

Undefined NCA options class.

Description

Undefined NCA options class.

Undefined NCA time window class.

Description

Undefined NCA time window class.

Undefined NCA options.

Description

Undefined NCA options.

Usage

UndefinedNCAOptions()

Value

an object of class undefined_nca_options

Create an undefined time window.

Description

Create an undefined time window.

Usage

UndefinedTimeWindow()

Value

undefined time window

Value at metric class.

Description

Value at metric class.

Value at (value at specific time).

Description

Value at (value at specific time).

Usage

ValueAt(
  variable = NULL,
  window = NULL,
  time = NULL,
  name = NULL,
  unit = NULL,
  stat_display = NULL,
  digits = NULL
)

Arguments

variable

dependent variable
window

time window on which this metric should be computed
time

what time to read the values. If not provided, last values from x will be returned.
name

custom metric name (will be exported into table headers)
unit

metric unit (will be exported into table headers if provided)
stat_display

statistics display, default is '{median} [{p5}-{p95}]' for continuous data or '{n} / {N} ({p}%)' for categorical data
digits

rounding digits definitions (integer, function, purrr-style lambda function or list of these, 1 item per statistic), see README

Value

an object of class value_at_metric