Package: pmxTools 1.6

pmxTools: Pharmacometric and Pharmacokinetic Toolkit

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) <https://www.facm.ucl.ac.be/cooperation/Vietnam/WBI-Vietnam-October-2011/Modelling/Monolix32_PKPD_library.pdf>); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).

Authors:Justin Wilkins [aut, cre], Bill Denney [aut], Rik Schoemaker [aut], Satyaprakash Nayak [ctb], Leonid Gibiansky [ctb], Andrew Hooker [ctb], E. Niclas Jonsson [ctb], Mats O. Karlsson [ctb], John Johnson [ctb]

pmxTools_1.6.tar.gz
pmxTools_1.6.zip(r-4.7)pmxTools_1.6.zip(r-4.6)pmxTools_1.6.zip(r-4.5)
pmxTools_1.6.tgz(r-4.6-any)pmxTools_1.6.tgz(r-4.5-any)
pmxTools_1.6.tar.gz(r-4.7-any)pmxTools_1.6.tar.gz(r-4.6-any)
pmxTools_1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
pmxTools/json (API)
NEWS

# Install 'pmxTools' in R:
install.packages('pmxTools', repos = c('https://kestrel99.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kestrel99/pmxtools/issues

Pkgdown/docs site:https://kestrel99.github.io

On CRAN:

Conda:

nonmempharmacokineticssimulation

6.98 score 35 stars 90 scripts 831 downloads 88 exports 45 dependencies

Last updated from:c7f691dfe4. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING173
source / vignettesOK203
linux-release-x86_64WARNING155
macos-release-arm64WARNING175
macos-oldrel-arm64WARNING226
windows-develWARNING124
windows-releaseWARNING118
windows-oldrelWARNING112
wasm-releaseOK116

Exports:blq_log_transblq_transbreaks_blq_generalcalc_derivedcalc_derived_1cptcalc_derived_2cptcalc_derived_3cptcalc_sd_1cmtcalc_sd_1cmt_linear_boluscalc_sd_1cmt_linear_infusioncalc_sd_1cmt_linear_oral_0calc_sd_1cmt_linear_oral_0_lagcalc_sd_1cmt_linear_oral_1calc_sd_1cmt_linear_oral_1_lagcalc_sd_2cmtcalc_sd_2cmt_linear_boluscalc_sd_2cmt_linear_infusioncalc_sd_2cmt_linear_oral_0calc_sd_2cmt_linear_oral_0_lagcalc_sd_2cmt_linear_oral_1calc_sd_2cmt_linear_oral_1_lagcalc_sd_3cmtcalc_sd_3cmt_linear_boluscalc_sd_3cmt_linear_infusioncalc_sd_3cmt_linear_oral_0calc_sd_3cmt_linear_oral_0_lagcalc_sd_3cmt_linear_oral_1calc_sd_3cmt_linear_oral_1_lagcalc_ss_1cmtcalc_ss_1cmt_linear_boluscalc_ss_1cmt_linear_infusioncalc_ss_1cmt_linear_oral_0calc_ss_1cmt_linear_oral_0_lagcalc_ss_1cmt_linear_oral_1calc_ss_1cmt_linear_oral_1_lagcalc_ss_2cmtcalc_ss_2cmt_linear_boluscalc_ss_2cmt_linear_infusioncalc_ss_2cmt_linear_oral_0calc_ss_2cmt_linear_oral_0_lagcalc_ss_2cmt_linear_oral_1calc_ss_2cmt_linear_oral_1_lagcalc_ss_3cmtcalc_ss_3cmt_linear_boluscalc_ss_3cmt_linear_infusioncalc_ss_3cmt_linear_oral_0calc_ss_3cmt_linear_oral_0_lagcalc_ss_3cmt_linear_oral_1calc_ss_3cmt_linear_oral_1_lagcount_nacut_quantiledatamapdgr_tableestimate_lloqfmt_signifftrans_blq_linearftrans_blq_loggcvgcv_convertget_aucget_est_tableget_omegaget_probinfoget_shrinkageget_sigmaget_thetagmitrans_blq_linearitrans_blq_loglabel_blqpcvpk_curveplot_distplot_nmprogressplot_scmread_nmread_nm_allread_nm_multi_tableread_nm_std_extread_nmcovread_nmextread_nmtablesread_scmrnmsample_omegasample_sigmasample_uncerttable_rtf

Dependencies:backportscheckmatechronclicpp11data.treedigestdistributionaldplyrfarvergenericsggdistggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSnlmenumDerivpatchworkpillarpkgconfigPKNCApurrrquadprogR6RColorBrewerRcpprlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrxml2

Drawing PK curves with pmxTools

Rendered frompk-curves.Rmdusingknitr::rmarkdownon Jun 01 2026.

Last update: 2025-08-25
Started: 2020-08-25

Readme and manuals

Help Manual

Help pageTopics
A transform for ggplot2 with data that may be below the lower limit of quantificationblq_log_trans blq_trans
Generate breaks for measurements below the limit of quantificationbreaks_blq_general
Calculate derived pharmacokinetic parameters for a 1-, 2-, or 3-compartment linear model.calc_derived calc_derived_1cpt calc_derived_2cpt calc_derived_3cpt
Calculate C(t) for a 1-compartment linear modelcalc_sd_1cmt calc_sd_1cmt_linear_bolus calc_sd_1cmt_linear_infusion calc_sd_1cmt_linear_oral_0 calc_sd_1cmt_linear_oral_0_lag calc_sd_1cmt_linear_oral_1 calc_sd_1cmt_linear_oral_1_lag
Calculate C(t) for a 2-compartment linear modelcalc_sd_2cmt calc_sd_2cmt_linear_bolus calc_sd_2cmt_linear_infusion calc_sd_2cmt_linear_oral_0 calc_sd_2cmt_linear_oral_0_lag calc_sd_2cmt_linear_oral_1 calc_sd_2cmt_linear_oral_1_lag
Calculate C(t) for a 3-compartment linear modelcalc_sd_3cmt calc_sd_3cmt_linear_bolus calc_sd_3cmt_linear_infusion calc_sd_3cmt_linear_oral_0 calc_sd_3cmt_linear_oral_0_lag calc_sd_3cmt_linear_oral_1 calc_sd_3cmt_linear_oral_1_lag
Calculate C(t) for a 1-compartment linear model at steady-statecalc_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 calc_ss_1cmt_linear_oral_1_lag
Calculate C(t) for a 2-compartment linear model at steady-statecalc_ss_2cmt calc_ss_2cmt_linear_bolus calc_ss_2cmt_linear_infusion calc_ss_2cmt_linear_oral_0 calc_ss_2cmt_linear_oral_0_lag calc_ss_2cmt_linear_oral_1 calc_ss_2cmt_linear_oral_1_lag
Calculate C(t) for a 3-compartment linear model at steady-statecalc_ss_3cmt calc_ss_3cmt_linear_bolus calc_ss_3cmt_linear_infusion calc_ss_3cmt_linear_oral_0 calc_ss_3cmt_linear_oral_0_lag calc_ss_3cmt_linear_oral_1 calc_ss_3cmt_linear_oral_1_lag
Count the number of NA values in a vector.count_na
Create quantile-based bins for continuous variablescut_quantile
Create a data map showing individual dosing and observation records over timedatamap
Generate a summary table of descriptive data for every individual in a dataset suitable for tabulation in a report.dgr_table
Estimate the lower limit of quantification (LLOQ) from a vectorestimate_lloq
Format a number with the correct number of significant digits and trailing zeroes.fmt_signif
Forward transformation for linear BLQ dataftrans_blq_linear ftrans_blq_log
Calculate a geometric coefficient of variation.gcv
Convert geometric variance or standard deviation to a geometric coefficient of variationgcv_convert
Calculate the area under the curve (AUC) for each subject over the time interval for dependent variables ('dv') using the trapezoidal rule.get_auc
Create a table of model parameter estimates from a NONMEM output object.get_est_table
Extract variability parameter estimates from a NONMEM output object.get_omega
Extract problem and estimation information from a NONMEM output object.get_probinfo
Extract shrinkage estimates from a NONMEM output object.get_shrinkage
Extract residual variability parameter estimates from a NONMEM output object.get_sigma
Extract structural model parameter estimates and associated information from a NONMEM output object.get_theta
Calculate geometric meangm
Inverse transformation for linear BLQ dataitrans_blq_linear itrans_blq_log
Label axes with censoring labels for BLQlabel_blq
Calculate percentage coefficient of variationpcv
Provide concentration-time curves.pk_curve
Plot a distribution as a hybrid containing a halfeye, a boxplot and jittered points.plot_dist
Plot NONMEM parameter estimation by iteration.plot_nmprogress
Visualize PsN SCM output.plot_scm
Read NONMEM 7.2+ output into a list of lists.read_nm
Read all NONMEM files for a single NONMEM run.read_nm_all
Read (single or) multiple NONMEM tables from a single fileread_nm_multi_table
Read a standard NONMEM extension fileread_nm_std_ext
Read in the NONMEM variance-covariance matrix.read_nmcov
Read NONMEM output into a list.read_nmext
Reads NONMEM output tables.read_nmtables
Read PsN SCM output into a format suitable for further use.read_scm
Read NONMEM 7.2+ output into an R object.rnm
Sample from the multivariate normal distribution using the OMEGA variance-covariance matrix to generate new sets of simulated ETAs from NONMEM output.sample_omega
Sample from the multivariate normal distribution using the SIGMA variance-covariance matrix to generate new sets of simulated EPSILONs from NONMEM output.sample_sigma
Sample from the multivariate normal distribution to generate new sets of parameters from NONMEM output.sample_uncert
Read NONMEM output into a list.table_rtf