Perform Pharmacokinetic Non-Compartmental Analysis

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Documentation for package ‘PKNCA’ version 0.6

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A B C E F G I M P R S T

PKNCA-package Compute noncompartmental pharmacokinetics

-- A --

add.interval.col Add columns for calculations within PKNCA intervals
adj.r.squared Calculate the adjusted r-squared value
AIC.list Assess the AIC for all models in a list of models
as.data.frame.PKNCAresults Extract the parameter results from a PKNCAresults and return them as a data frame.

-- B --

business.mean Generate functions to do the named function (e.g. mean) applying the business rules.

-- C --

check.conc.time Verify that the concentration and time are valid
check.conversion Check that the conversion to a data type does not change the number of NA values
check.interval.deps Take in a single row of an interval specification and return that row updated with any additional calculations that must be done to fulfil all dependencies.
check.interval.specification Check the formatting of a calculation interval specification data frame.
choose.auc.intervals Choose intervals to compute AUCs from time and dosing information
clean.conc.blq Handle BLQ values in the concentration measurements as requested by the user.
clean.conc.na Handle NA values in the concentration measurements as requested by the user.
count.non.missing Count the number of values that are not NA

-- E --

extrapolate.conc Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.

-- F --

find.tau Find the repeating interval within a vector of doses
findOperator Find the first occurrence of an operator in a formula and return the left, right, or both sides of the operator.
formula.parseFormula Convert the parsed formula back into the original
formula.PKNCAconc Extract the formula from a PKNCAconc object.
formula.PKNCAdose Extract the formula from a PKNCAconc object.

-- G --

geocv Compute the geometric mean, sd, and CV
geomean Compute the geometric mean, sd, and CV
geosd, Compute the geometric mean, sd, and CV
get.best.model Extract the best model from a list of models using AIC.list.
get.first.model Get the first model from a list of models
get.interval.cols Get the columns that can be used in an interval specification
getData.PKNCAconc Extract all the original data from a PKNCAconc or PKNCAdose object
getData.PKNCAdose Extract all the original data from a PKNCAconc or PKNCAdose object
getDepVar Get the dependent variable (left hand side of the formula) from a PKNCA object.
getGroups.PKNCAconc Get the groups (right hand side after the '|' from a PKNCA object.
getGroups.PKNCAdose Get the groups (right hand side after the '|' from a PKNCA object.
getGroups.PKNCAresults Get the groups (right hand side after the '|' from a PKNCA object.
getIndepVar Get the independent variable (right hand side of the formula) from a PKNCA object.

-- I --

interp.extrap.conc Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.
interpolate.conc Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.

-- M --

merge.splitByData Merge lists of data to make a list of lists.
model.frame.PKNCAconc Extract the columns used in the formula (in order) from a PKNCAconc or PKNCAdose object.
model.frame.PKNCAdose Extract the columns used in the formula (in order) from a PKNCAconc or PKNCAdose object.

-- P --

parseFormula Parse a formula into its component parts.
pk.business Run any function with a maximum missing fraction of X and 0s possibly counting as missing. The maximum fraction missing comes from 'PKNCA.options("max.missing")'.
pk.calc.auc A compute the Area Under the (Moment) Curve
pk.calc.auc.all A compute the Area Under the (Moment) Curve
pk.calc.auc.inf A compute the Area Under the (Moment) Curve
pk.calc.auc.last A compute the Area Under the (Moment) Curve
pk.calc.aucpext Calculate the AUC percent extrapolated
pk.calc.aumc A compute the Area Under the (Moment) Curve
pk.calc.aumc.all A compute the Area Under the (Moment) Curve
pk.calc.aumc.inf A compute the Area Under the (Moment) Curve
pk.calc.aumc.last A compute the Area Under the (Moment) Curve
pk.calc.auxc A compute the Area Under the (Moment) Curve
pk.calc.cav Calculate the average concentration during an interval.
pk.calc.cl Calculate the (observed oral) clearance
pk.calc.clast.obs Determine the last observed concentration above the limit of quantification (LOQ).
pk.calc.cmax Determine maximum observed PK concentration
pk.calc.cmin Determine maximum observed PK concentration
pk.calc.ctrough Determine the trough (predose) concentration
pk.calc.f Calculate the absolute (or relative) bioavailability
pk.calc.half.life Compute the half-life and associated parameters
pk.calc.kel Calculate the elimination rate (Kel)
pk.calc.mrt Calcuate the mean residence time (MRT)
pk.calc.ptr Determine the peak-to-trough ratio
pk.calc.tfirst Determine time of last observed concentration above the limit of quantification.
pk.calc.thalf.eff Calculate the effective half-life
pk.calc.tlag Determine the observed lag time (time before the first concentration above the limit of quantification or above the first concentration in the interval)
pk.calc.tlast Determine time of last observed concentration above the limit of quantification.
pk.calc.tmax Determine time of maximum observed PK concentration
pk.calc.vss Calculate the steady-state volume of distribution (Vss)
pk.calc.vz Calculate the terminal volume of distribution (Vz)
pk.nca Compute NCA parameters for each interval for each subject.
pk.nca.interval Compute all PK parameters for a single concentration-time data set
pk.tss Compute the time to steady-state (tss)
pk.tss.data.prep Clean up the time to steady-state parameters and return a data frame for use by the tss calculators.
pk.tss.monoexponential Compute the time to steady state using nonlinear, mixed-effects modeling of trough concentrations.
pk.tss.monoexponential.individual A helper function to estimate individual and single outputs for monoexponential time to steady-state.
pk.tss.monoexponential.population A helper function to estimate population and popind outputs for monoexponential time to steady-state.
pk.tss.stepwise.linear Compute the time to steady state using stepwise test of linear trend
PKNCA Compute noncompartmental pharmacokinetics
PKNCA.choose.option Choose either the value from an option list or the current set value for an option.
PKNCA.options Set default options for PKNCA functions
PKNCA.set.summary Define how NCA parameters are summarized.
PKNCAconc Create a PKNCAconc object
PKNCAdata Create a PKNCAdata object.
PKNCAdose Create a PKNCAdose object
PKNCAresults Generate a PKNCAresults object
plot.PKNCAconc Plot a PKNCAconc object
plot.PKNCAdata Plot a PKNCAconc object
print.PKNCAconc Print and/or summarize a PKNCAconc or PKNCAdose object.
print.PKNCAdata Print a PKNCAdata object
print.PKNCAdose Print and/or summarize a PKNCAconc or PKNCAdose object.

-- R --

roundingSummarize During the summarization of PKNCAresults, do the rounding of values based on the instructions given.
roundString Round a value to a defined number of digits printing out trailing zeros, if applicable.

-- S --

sapplyBy Similar to lapplyBy but returning a data frame
signifString Round a value to a defined number of significant digits printing out trailing zeros, if applicable.
sort.interval.cols Sort the interval columns by dependencies.
summary.PKNCAconc Print and/or summarize a PKNCAconc or PKNCAdose object.
summary.PKNCAdata Summarize a PKNCAdata object showing important details about the concentration, dosing, and interval information.
summary.PKNCAdose Print and/or summarize a PKNCAconc or PKNCAdose object.
summary.PKNCAresults Summarize PKNCA results
superposition Compute noncompartmental superposition for repeated dosing
superposition.numeric Compute noncompartmental superposition for repeated dosing
superposition.PKNCAconc Compute noncompartmental superposition for repeated dosing

-- T --

tss.monoexponential.generate.formula A helper function to generate the formula and starting values for the parameters in monoexponential models.