Package: qgcomp 2.19.0

Alexander Keil

qgcomp: Quantile G-Computation

G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. This approach estimates a regression line corresponding to the expected change in the outcome (on the link basis) given a simultaneous increase in the quantile-based category for all exposures. Works with continuous, binary, and right-censored time-to-event outcomes. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.

Authors:Alexander Keil [aut, cre]

qgcomp_2.19.0.tar.gz
qgcomp_2.19.0.zip(r-4.7)qgcomp_2.19.0.zip(r-4.6)qgcomp_2.19.0.zip(r-4.5)
qgcomp_2.19.0.tgz(r-4.6-any)qgcomp_2.19.0.tgz(r-4.5-any)
qgcomp_2.19.0.tar.gz(r-4.7-any)qgcomp_2.19.0.tar.gz(r-4.6-any)
qgcomp_2.19.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
qgcomp/json (API)

# Install 'qgcomp' in R:
install.packages('qgcomp', repos = c('https://alexpkeil1.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alexpkeil1/qgcomp/issues

Datasets:

On CRAN:

Conda:

exposureexposure-mixtureexposure-mixturesquantile-gcomputationsurvival

8.85 score 43 stars 2 packages 99 scripts 1.0k downloads 3 mentions 40 exports 83 dependencies

Last updated from:f6c3c66721. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING277
source / vignettesOK326
linux-release-x86_64WARNING270
macos-release-arm64WARNING190
macos-oldrel-arm64WARNING167
windows-develWARNING199
windows-releaseWARNING219
windows-oldrelWARNING213
wasm-releaseOK145

Exports:.qgcomp_object.qgcomp_object_addcoxmsm_fithomogeneity_testhurdlemsm_fit.controljoint_testmice.impute.leftcenslognormmice.impute.tobitmodelbound.bootmodelbound.eemsm_fitmsm_multinomial_fitmsm.predictpointwisebound.bootpointwisebound.nobootqgcompqgcomp.bootqgcomp.cch.nobootqgcomp.cox.bootqgcomp.cox.nobootqgcomp.eeqgcomp.glm.bootqgcomp.glm.eeqgcomp.glm.nobootqgcomp.hurdle.bootqgcomp.hurdle.nobootqgcomp.multinomial.bootqgcomp.multinomial.nobootqgcomp.nobootqgcomp.partialsqgcomp.survcurve.bootqgcomp.tobit.nobootqgcomp.zi.bootqgcomp.zi.nobootquantizese_combsimdata_quantizedsplit_datavc_combzimsm_fit.control

Dependencies:abindAERarmbackportsbootbroomcarcarDataclicodacodetoolscolorspacecowplotcpp11DerivdigestdoBydplyrfarverforecastFormulafracdifffuturefuture.applygenericsggplot2globalsgluegridExtragtableisobandlabelinglatticelifecyclelistenvlme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigpsclpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrootSolveS7sandwichscalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

The qgcomp package: g-computation on exposure quantiles

Alexander Keil

Rendered fromqgcomp-vignette.Rmdusingknitr::knitron Jun 03 2026.

Last update: 2025-03-27
Started: 2018-10-25

Advanced topics for the qgcomp package: time-to-event, clustering, partial effects, weighting, missing data

Alexander Keil

Rendered fromqgcomp-advanced-vignette.Rmdusingknitr::knitron Jun 03 2026.

Last update: 2025-03-27
Started: 2024-06-29

Basic topics for the qgcomp package: g-computation on exposure quantiles

Alexander Keil

Rendered fromqgcomp-basic-vignette.Rmdusingknitr::knitron Jun 03 2026.

Last update: 2025-03-27
Started: 2024-06-29

Readme and manuals

Help Manual

Help pageTopics
Creating a 'qgcompfit' object.qgcomp_object
Adding objects to a 'qgcompfit' object.qgcomp_object_add
Check for valid model terms in a qgcomp fitchecknames
Marginal structural Cox model (MSM) fitting within quantile g-computationcoxmsm_fit
Glance at a qgcompfit objectglance.qgcompfit
Hypothesis testing about a joint effect of exposures on a multinomial outcomehomogeneity_test homogeneity_test.qgcompmultfit
Secondary prediction method for the (hurdle) qgcomp MSM.hurdlemsm_fit
Control of fitting parameters for zero inflated MSMshurdlemsm_fit.control
Hypothesis testing about a joint effect of exposures on a multinomial outcomejoint_test joint_test.qgcompmultfit
Well water datametals
Imputation for limits of detection problemsmice.impute.leftcenslognorm mice.impute.tobit
Estimating qgcomp regression line confidence boundsmodelbound.boot
Estimating qgcomp regression line confidence boundsmodelbound.ee
Fitting marginal structural model (MSM) within quantile g-computationmsm_fit
Fitting marginal structural model (MSM) within quantile g-computationmsm_multinomial_fit
Secondary prediction method for the (non-survival) qgcomp MSM.msm.predict
Default plotting method for a qgcompfit objectplot.qgcompfit plot.qgcompmultfit
Estimating pointwise comparisons for qgcomp.glm.boot objectspointwisebound.boot
Estimating pointwise comparisons for qgcomp.glm.noboot objectspointwisebound.noboot
Default prediction method for a qgcompfit object (non-survival outcomes only)predict.qgcompfit
Default printing method for a qgcompfit objectprint.qgcompfit
Default printing method for a qgcomppartialavg objectprint.qgcomppartialavg
Default printing method for a qgcomppartialavg objectprint.qgcomppartialavg_boot
Quantile g-computation for continuous, binary, count, and censored survival outcomesqgcomp
Quantile g-computation for survival outcomes in a case-cohort design under linearity/additivityqgcomp.cch.noboot
Quantile g-computation for survival outcomesqgcomp.cox.boot
Quantile g-computation for survival outcomes under linearity/additivityqgcomp.cox.noboot
Quantile g-computation for continuous and binary outcomesqgcomp.boot qgcomp.glm.boot
Quantile g-computation for continuous and binary outcomesqgcomp.ee qgcomp.glm.ee
Quantile g-computation for continuous, binary, and count outcomes under linearity/additivityqgcomp.glm.noboot qgcomp.noboot
Quantile g-computation for hurdle count outcomesqgcomp.hurdle.boot
Quantile g-computation for hurdle count outcomes under linearity/additivityqgcomp.hurdle.noboot
Quantile g-computation for multinomial outcomesqgcomp.multinomial.boot
Quantile g-computation for multinomial outcomesqgcomp.multinomial.noboot
Partial effect sizes, confidence intervals, hypothesis testsqgcomp.partials
Survival curve data from a qgcomp survival fitqgcomp.survcurve.boot
Quantile g-computation for left-censored outcomesqgcomp.tobit.noboot
Quantile g-computation for zero-inflated count outcomesqgcomp.zi.boot
Quantile g-computation for zero-inflated count outcomes under linearity/additivityqgcomp.zi.noboot
Quantizing exposure dataquantize
Calculate standard error of weighted linear combination of random variablesse_comb
Simulate quantized exposures for testing methodssimdata_quantized
Perform sample splittingsplit_data
Summarize gcompmultfit objectsummary.qgcompmultfit
Tidy method for qgcompfit objecttidy.qgcompfit
Calculate covariance matrix between one random variable and a linear combination of random variablesvc_comb
Secondary prediction method for the (zero-inflated) qgcomp MSM.zimsm_fit
Control of fitting parameters for zero inflated MSMszimsm_fit.control