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pmxTools

Pharmacometric Tools for Modeling & Simulation

Developed by Justin Wilkins, Bill Denney and Rik Schoemaker, with contributions from a host of others.

Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).

To install:

devtools::install_github("kestrel99/pmxTools")

or download directly from CRAN.

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Version

Install

install.packages('pmxTools')

Monthly Downloads

420

Version

1.3

License

GPL-2

Issues

4

Pull Requests

3

Stars

32

Forks

10

Maintainer

Justin Wilkins

Last Published

February 21st, 2023

Functions in pmxTools (1.3)

breaks_blq_general

Generate breaks for measurements below the limit of quantification
calc_ss_1cmt

Calculate C(t) for a 1-compartment linear model at steady-state
calc_sd_2cmt

Calculate C(t) for a 2-compartment linear model
gcv

Calculate a geometric coefficient of variation.
get_probinfo

Extract problem and estimation information from a NONMEM output object.
fmt_signif

Format a number with the correct number of significant digits and trailing zeroes.
get_est_table

Create a table of model parameter estimates from a NONMEM output object.
estimate_lloq

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

Convert geometric variance or standard deviation to a geometric coefficient of variation
get_omega

Extract variability parameter estimates from a NONMEM output object.
dgr_table

Generate a summary table of descriptive data for every individual in a dataset suitable for tabulation in a report.
get_auc

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

Forward transformation for linear BLQ data
gm

Calculate geometric mean
pcv

Calculate percentage coefficient of variation
get_sigma

Extract residual variability parameter estimates from a NONMEM output object.
itrans_blq_linear

Inverse transformation for linear BLQ data
get_shrinkage

Extract shrinkage estimates from a NONMEM output object.
get_theta

Extract structural model parameter estimates and associated information from a NONMEM output object.
pk_curve

Provide concentration-time curves.
plot_dist

Plot a distribution as a hybrid containing a halfeye, a boxplot and jittered points.
plot_scm

Visualize PsN SCM output.
read_nm

Read NONMEM 7.2+ output into a list of lists.
plot_nmprogress

Plot NONMEM parameter estimation by iteration.
read_nmcov

Read in the NONMEM variance-covariance matrix.
read_nm_multi_table

Read (single or) multiple NONMEM tables from a single file
read_nm_std_ext

Read a standard NONMEM extension file
label_blq

Label axes with censoring labels for BLQ
read_scm

Read PsN SCM output into a format suitable for further use.
read_nm_all

Read all NONMEM files for a single NONMEM run.
rnm

Read NONMEM 7.2+ output into an R object.
read_nmtables

Reads NONMEM output tables.
sample_uncert

Sample from the multivariate normal distribution to generate new sets of parameters from NONMEM output.
sample_omega

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

Read NONMEM output into a list.
table_rtf

Read NONMEM output into a list.
sample_sigma

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

Calculate derived pharmacokinetic parameters for a 1-, 2-, or 3-compartment linear model.
count_na

Count the number of NA values in a vector.
calc_sd_1cmt

Calculate C(t) for a 1-compartment linear model
blq_trans

A transform for ggplot2 with data that may be below the lower limit of quantification
calc_ss_3cmt

Calculate C(t) for a 3-compartment linear model at steady-state
calc_sd_3cmt

Calculate C(t) for a 3-compartment linear model
calc_ss_2cmt

Calculate C(t) for a 2-compartment linear model at steady-state