Function reference • PKNCA

All functions
`AIC(<list>)`	Assess the AIC for all models in a list of models
`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.options.describe()`	Describe a PKNCA.options option by name.
`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
`add.interval.col()`	Add columns for calculations within PKNCA intervals
`addProvenance()`	Add a hash and associated information to enable checking object provenance.
`adj.r.squared()`	Calculate the adjusted r-squared value
`as.data.frame(<PKNCAresults>)`	Extract the parameter results from a PKNCAresults and return them as a data frame.
`business.mean()` `business.sd()` `business.cv()` `business.geomean()` `business.geocv()` `business.min()` `business.max()` `business.median()` `business.range()`	Generate functions to do the named function (e.g. mean) applying the business rules.
`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 fulfill all dependencies.
`check.interval.specification()`	Check the formatting of a calculation interval specification data frame.
`checkProvenance()`	Check the hash of an object to confirm its provenance.
`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.
`exclude()`	Exclude data points or results from calculations or summarization.
`exclude_nca_span.ratio()` `exclude_nca_max.aucinf.pext()` `exclude_nca_min.hl.r.squared()`	Exclude NCA parameters based on examining the parameter set.
`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.
`fit_half_life()`	Perform the half-life fit given the data. The function simply fits the data without any validation. No selection of points or any other components are done.
`formula(<PKNCAconc>)` `formula(<PKNCAdose>)`	Extract the formula from a PKNCAconc object.
`formula(<parseFormula>)`	Convert the parsed formula back into the original
`geomean()` `geosd()` `geocv()`	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
`get.parameter.deps()`	Get all columns that depend on a parameter
`getAttributeColumn()`	Retrieve the value of an attribute column.
`getColumnValueOrNot()`	Get the value from a column in a data frame if the value is a column there, otherwise, the value should be a scalar or the length of the data.
`getData(<PKNCAconc>)` `getData(<PKNCAdose>)`	Extract all the original data from a PKNCAconc or PKNCAdose object
`getData(<PKNCAdata>)`	Extract all the original data from a PKNCAconc or PKNCAdose object
`getData(<PKNCAresults>)`	Extract all the original data from a PKNCAconc or PKNCAdose object
`getDataName()`	Get the name of the element containing the data for the current object.
`getDepVar()`	Get the dependent variable (left hand side of the formula) from a PKNCA object.
`getGroups(<PKNCAconc>)` `getGroups(<PKNCAdose>)` `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.
`group_vars.PKNCAconc()` `group_vars.PKNCAdose()`	Get grouping variables for a PKNCA object
`interp.extrap.conc()` `interpolate.conc()` `extrapolate.conc()` `interp.extrap.conc.dose()`	Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.
`model.frame(<PKNCAconc>)` `model.frame(<PKNCAdose>)`	Extract the columns used in the formula (in order) from a PKNCAconc or PKNCAdose object.
`normalize_exclude()`	Normalize the exclude column by setting blanks to NA
`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.ae()`	Calculate amount excreted (typically in urine or feces)
`pk.calc.aucint()` `pk.calc.aucint.last()` `pk.calc.aucint.all()` `pk.calc.aucint.inf.obs()` `pk.calc.aucint.inf.pred()`	Calculate the AUC over an interval with interpolation and/or extrapolation of concentrations for the beginning and end of the interval.
`pk.calc.aucpext()`	Calculate the AUC percent extrapolated
`pk.calc.auxc()` `pk.calc.auc()` `pk.calc.auc.last()` `pk.calc.auc.inf()` `pk.calc.auc.inf.obs()` `pk.calc.auc.inf.pred()` `pk.calc.auc.all()` `pk.calc.aumc()` `pk.calc.aumc.last()` `pk.calc.aumc.inf()` `pk.calc.aumc.inf.obs()` `pk.calc.aumc.inf.pred()` `pk.calc.aumc.all()`	A compute the Area Under the (Moment) Curve
`pk.calc.c0()` `pk.calc.c0.method.logslope()` `pk.calc.c0.method.c0()` `pk.calc.c0.method.c1()` `pk.calc.c0.method.set0()` `pk.calc.c0.method.cmin()`	Estimate the concentration at dosing time for an IV bolus dose.
`pk.calc.cav()`	Calculate the average concentration during an interval.
`pk.calc.ceoi()`	Determine the concentration at the end of infusion
`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.clr()`	Calculate renal clearance
`pk.calc.cmax()` `pk.calc.cmin()`	Determine maximum observed PK concentration
`pk.calc.ctrough()`	Determine the trough (predose) concentration
`pk.calc.deg.fluc()`	Determine the degree of fluctuation
`pk.calc.dn()`	Determine dose normalized NCA parameter
`pk.calc.f()`	Calculate the absolute (or relative) bioavailability
`pk.calc.fe()`	Calculate fraction excreted (typically in urine or feces)
`pk.calc.half.life()`	Compute the half-life and associated parameters
`pk.calc.kel()`	Calculate the elimination rate (Kel)
`pk.calc.mrt()` `pk.calc.mrt.iv()`	Calculate the mean residence time (MRT) for single-dose data or linear multiple-dose data.
`pk.calc.mrt.md()`	Calculate the mean residence time (MRT) for multiple-dose data with nonlinear kinetics.
`pk.calc.ptr()`	Determine the peak-to-trough ratio
`pk.calc.swing()`	Determine the PK swing
`pk.calc.thalf.eff()`	Calculate the effective half-life
`pk.calc.time_above()`	Determine time at or above a set value
`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()` `pk.calc.tfirst()`	Determine time of last observed concentration above the limit of quantification.
`pk.calc.tmax()`	Determine time of maximum observed PK concentration
`pk.calc.vd()`	Calculate the volume of distribution (Vd) or observed volume of distribution (Vd/F)
`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.nca.intervals()`	Compute NCA for multiple intervals
`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
`print(<PKNCAconc>)` `summary(<PKNCAconc>)` `print(<PKNCAdose>)` `summary(<PKNCAdose>)`	Print and/or summarize a PKNCAconc or PKNCAdose object.
`print(<PKNCAdata>)`	Print a PKNCAdata object
`print(<provenance>)`	Print the summary of a provenance object
`print(<summary_PKNCAresults>)`	Print the results summary
`roundString()`	Round a value to a defined number of digits printing out trailing zeros, if applicable.
`roundingSummarize()`	During the summarization of PKNCAresults, do the rounding of values based on the instructions given.
`setAttributeColumn()`	Add an attribute to an object where the attribute is added as a name to the names of the object.
`setDuration()`	Set the duration of dosing or measurement
`setExcludeColumn()`	Set the exclude parameter on an object
`setRoute()`	Set the dosing route
`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(<PKNCAdata>)`	Summarize a PKNCAdata object showing important details about the concentration, dosing, and interval information.
`summary(<PKNCAresults>)`	Summarize PKNCA results
`superposition()`	Compute noncompartmental superposition for repeated dosing
`time_calc()`	Times relative to an event (typically dosing)
`tss.monoexponential.generate.formula()`	A helper function to generate the formula and starting values for the parameters in monoexponential models.

Reference

All functions