Package index • pmxTools

All functions

blq_trans() blq_log_trans(): A transform for ggplot2 with data that may be below the lower limit of quantification

breaks_blq_general(): Generate breaks for measurements below the limit of quantification

calc_derived() calc_derived_1cpt() calc_derived_2cpt() calc_derived_3cpt(): Calculate derived pharmacokinetic parameters for a 1-, 2-, or 3-compartment linear model.

calc_sd_1cmt() calc_sd_1cmt_linear_bolus() calc_sd_1cmt_linear_oral_1_lag() calc_sd_1cmt_linear_infusion() calc_sd_1cmt_linear_oral_0() calc_sd_1cmt_linear_oral_1() calc_sd_1cmt_linear_oral_0_lag(): Calculate C(t) for a 1-compartment linear model

calc_sd_2cmt() calc_sd_2cmt_linear_bolus() calc_sd_2cmt_linear_oral_1_lag() calc_sd_2cmt_linear_infusion() calc_sd_2cmt_linear_oral_0_lag() calc_sd_2cmt_linear_oral_1() calc_sd_2cmt_linear_oral_0(): Calculate C(t) for a 2-compartment linear model

calc_sd_3cmt() calc_sd_3cmt_linear_bolus() calc_sd_3cmt_linear_oral_1_lag() calc_sd_3cmt_linear_infusion() calc_sd_3cmt_linear_oral_0() calc_sd_3cmt_linear_oral_0_lag() calc_sd_3cmt_linear_oral_1(): Calculate C(t) for a 3-compartment linear model

calc_ss_1cmt() calc_ss_1cmt_linear_bolus() calc_ss_1cmt_linear_infusion() calc_ss_1cmt_linear_oral_0() calc_ss_1cmt_linear_oral_0_lag() calc_ss_1cmt_linear_oral_1_lag() calc_ss_1cmt_linear_oral_1(): Calculate C(t) for a 1-compartment linear model at steady-state

calc_ss_2cmt() calc_ss_2cmt_linear_bolus() calc_ss_2cmt_linear_infusion() calc_ss_2cmt_linear_oral_0() calc_ss_2cmt_linear_oral_1_lag() calc_ss_2cmt_linear_oral_0_lag() calc_ss_2cmt_linear_oral_1(): Calculate C(t) for a 2-compartment linear model at steady-state

calc_ss_3cmt() calc_ss_3cmt_linear_bolus() calc_ss_3cmt_linear_oral_1_lag() calc_ss_3cmt_linear_infusion() calc_ss_3cmt_linear_oral_0() calc_ss_3cmt_linear_oral_0_lag() calc_ss_3cmt_linear_oral_1(): Calculate C(t) for a 3-compartment linear model at steady-state

count_na(): Count the number of NA values in a vector.

dgr_table(): Generate a summary table of descriptive data for every individual in a dataset suitable for tabulation in a report.

estimate_lloq(): Estimate the lower limit of quantification (LLOQ) from a vector

fmt_signif(): Format a number with the correct number of significant digits and trailing zeroes.

ftrans_blq_linear() ftrans_blq_log(): Forward transformation for linear BLQ data

gcv(): Calculate a geometric coefficient of variation.

gcv_convert(): Convert geometric variance or standard deviation to a geometric coefficient of variation

get_auc(): Calculate the area under the curve (AUC) for each subject over the time interval for dependent variables (dv) using the trapezoidal rule.

get_est_table(): Create a table of model parameter estimates from a NONMEM output object.

get_omega(): Extract variability parameter estimates from a NONMEM output object.

get_probinfo(): Extract problem and estimation information from a NONMEM output object.

get_shrinkage(): Extract shrinkage estimates from a NONMEM output object.

get_sigma(): Extract residual variability parameter estimates from a NONMEM output object.

get_theta(): Extract structural model parameter estimates and associated information from a NONMEM output object.

gm(): Calculate geometric mean

itrans_blq_linear() itrans_blq_log(): Inverse transformation for linear BLQ data

label_blq(): Label axes with censoring labels for BLQ

pcv(): Calculate percentage coefficient of variation

pk_curve(): Provide concentration-time curves.

plot_dist(): Plot a distribution as a hybrid containing a halfeye, a boxplot and jittered points.

plot_nmprogress(): Plot NONMEM parameter estimation by iteration.

plot_scm(): Visualize PsN SCM output.

read_nm(): Read NONMEM 7.2+ output into a list of lists.

read_nm_all(): Read all NONMEM files for a single NONMEM run.

read_nm_multi_table(): Read (single or) multiple NONMEM tables from a single file

read_nm_std_ext(): Read a standard NONMEM extension file

read_nmcov(): Read in the NONMEM variance-covariance matrix.

read_nmext(): Read NONMEM output into a list.

read_nmtables(): Reads NONMEM output tables.

read_scm(): Read PsN SCM output into a format suitable for further use.

rnm(): Read NONMEM 7.2+ output into an R object.

sample_omega(): Sample from the multivariate normal distribution using the OMEGA variance-covariance matrix to generate new sets of simulated ETAs from NONMEM output.

sample_sigma(): Sample from the multivariate normal distribution using the SIGMA variance-covariance matrix to generate new sets of simulated EPSILONs from NONMEM output.

sample_uncert(): Sample from the multivariate normal distribution to generate new sets of parameters from NONMEM output.

table_rtf(): Read NONMEM output into a list.