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PKNCA-package
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.
PKNCA_impute_fun_list()
Separate out a vector of PKNCA imputation methods into a list of functions
PKNCA_impute_method_start_conc0()
PKNCA_impute_method_start_cmin()
PKNCA_impute_method_start_predose()
Methods for imputation of data with PKNCA
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
any_sparse_dense_in_interval()
Determine if there are any sparse or dense calculations requested within an interval
as.data.frame(<PKNCAresults> )
Extract the parameter results from a PKNCAresults and return them as a data.frame.
as_PKNCAconc()
as_PKNCAdose()
as_PKNCAdata()
as_PKNCAresults()
Convert an object into a PKNCAconc object
as_sparse_pk()
Generate a sparse_pk object
assert_PKNCAdata()
Assert that an object is a PKNCAdata object
assert_aucmethod()
Assert that a value is a valid AUC method
assert_conc()
assert_time()
assert_conc_time()
Verify that concentration measurements are valid
assert_dosetau()
Assert that a value is a dosing interval
assert_intervals()
Assert Intervals
assert_intervaltime_single()
Assert that an interval is accurately defined as an interval, and return the interval
assert_lambdaz()
Assert that a lambda.z value is valid
assert_number_between()
Confirm that a value is greater than another value
assert_numeric_between()
Confirm that a value is greater than another value
auc_integrate()
Support function for AUC integration
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.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
choose_interval_method()
Choose how to interpolate, extrapolate, or integrate data in each concentration interval
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.
cov_holder()
Calculate the covariance for two time points with sparse sampling
check.conc.time()
The following functions are defunct
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.
filter(<PKNCAresults> )
filter(<PKNCAconc> )
filter(<PKNCAdose> )
dplyr filtering for PKNCA
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.
geomean()
geosd()
geocv()
Compute the geometric mean, sd, and CV
get.best.model()
Extract the best model from a list of models using the AIC.
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.
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(<PKNCAdata> )
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.
get_impute_method()
Get the impute function from either the intervals column or from the method
group_by(<PKNCAresults> )
group_by(<PKNCAconc> )
group_by(<PKNCAdose> )
ungroup(<PKNCAresults> )
ungroup(<PKNCAconc> )
ungroup(<PKNCAdose> )
dplyr grouping for PKNCA
group_vars(<PKNCAconc> )
group_vars(<PKNCAdose> )
Get grouping variables for a PKNCA object
inner_join(<PKNCAresults> )
left_join(<PKNCAresults> )
right_join(<PKNCAresults> )
full_join(<PKNCAresults> )
inner_join(<PKNCAconc> )
left_join(<PKNCAconc> )
right_join(<PKNCAconc> )
full_join(<PKNCAconc> )
inner_join(<PKNCAdose> )
left_join(<PKNCAdose> )
right_join(<PKNCAdose> )
full_join(<PKNCAdose> )
dplyr joins for PKNCA
interp.extrap.conc()
interpolate.conc()
extrapolate.conc()
interp.extrap.conc.dose()
Interpolate concentrations between measurements or extrapolate concentrations after the last measurement.
interpolate_conc_linear()
interpolate_conc_log()
extrapolate_conc_lambdaz()
Interpolate or extrapolate concentrations using the provided method
is_sparse_pk()
Is a PKNCA object used for sparse PK?
model.frame(<PKNCAconc> )
model.frame(<PKNCAdose> )
Extract the columns used in the formula (in order) from a PKNCAconc or PKNCAdose object.
mutate(<PKNCAresults> )
mutate(<PKNCAconc> )
mutate(<PKNCAdose> )
dplyr mutate-based modification for PKNCA
normalize_exclude()
Normalize the exclude column by setting blanks to NA
parse_formula_to_cols()
Convert a formula representation to the columns for input data
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.aucabove()
Calculate the AUC above a given concentration
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.auciv()
pk.calc.auciv_pbext()
Calculate AUC for intravenous dosing
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.count_conc()
Count the number of concentration measurements in an interval
pk.calc.cstart()
Determine the concentration at the beginning of the interval
pk.calc.ctrough()
Determine the trough (end of interval) 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.sparse_auc()
pk.calc.sparse_auclast()
Calculate AUC and related parameters using sparse NCA methods
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.totdose()
Extract the dose used for calculations
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
pk_nca_result_to_df()
Convert the grouping info and list of results for each group into a results data.frame
pknca_find_units_param()
Find NCA parameters with a given unit type
pknca_unit_conversion()
Perform unit conversion (if possible) on PKNCA results
pknca_units_add_paren()
Add parentheses to a unit value, if needed
pknca_units_table()
Create a unit assignment and conversion table
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
set_intervals()
Set Intervals
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.
sparse_auc_weight_linear()
Calculate the weight for sparse AUC calculation with the linear-trapezoidal rule
sparse_mean()
Calculate the mean concentration at all time points for use in sparse NCA calculations
sparse_pk_attribute()
Set or get a sparse_pk object attribute
sparse_to_dense_pk()
Extract the mean concentration-time profile as a data.frame
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.
var_sparse_auc()
Calculate the variance for the AUC of sparsely sampled PK