Package: rqlm 4.3-2

rqlm: Modified Poisson Regression for Binary Outcome and Related Methods

Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2026)<doi:10.1016/j.spl.2026.110698>, and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals.

Authors:Hisashi Noma [aut, cre]

rqlm_4.3-2.tar.gz
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manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
rqlm/json (API)

# Install 'rqlm' in R:
install.packages('rqlm', repos = c('https://nomahi.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • exdata01 - A simulated example dataset
  • exdata02 - A simulated example dataset
  • exdata03 - A simulated example dataset with missing covariates
  • exdata04 - A simulated example dataset for target trial emulation
  • mch - A cluster-randomised trial dataset for the maternal and child health handbook

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.85 score 4 scripts 242 downloads 14 exports 64 dependencies

Last updated from:d8c19098ed. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK166
source / vignettesOK161
linux-release-x86_64OK149
macos-release-arm64OK125
macos-oldrel-arm64OK102
windows-develOK114
windows-releaseOK137
windows-oldrelOK119
wasm-releaseOK100

Exports:bsci.lsbsci.poiscoeffmi_glmmi_rqlmqesci.lsqesci.poisqlogistrqlmstabwtstabwtlongstabwtmultiSumStatttemsm

Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayondplyrforcatsforeachgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmiceminqamitmlnlmenloptrnnetnumDerivordinalpanpillarpkgconfigprettyunitsprogresspurrrR6rbibutilsRcppRcppEigenRdpackreadrreformulasrlangrpartsandwichshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithrzoo

Readme and manuals

Help Manual

Help pageTopics
The 'rqlm' package.rqlm-package
Calculating bootstrap confidence interval for modified least-squares regression based on the quasi-score statisticbsci.ls
Calculating bootstrap confidence interval for modified Poisson regression based on the quasi-score statisticbsci.pois
Computation of the ordinary confidence intervals and P-values using the model variance estimatorcoeff
A simulated example datasetexdata01
A simulated example datasetexdata02
A simulated example dataset with missing covariatesexdata03
A simulated example dataset for target trial emulationexdata04
A cluster-randomised trial dataset for the maternal and child health handbookmch
Multiple imputation analysis for the generalized linear modelmi_glm
Multiple imputation analysis for modified Poisson and least-squares regressionsmi_rqlm
Calculating confidence interval for modified least-squares regression based on the quasi-score testqesci.ls
Calculating confidence interval for modified Poisson regression based on the quasi-score testqesci.pois
Augmented (modified) logistic regression analyses for estimating risk ratioqlogist
Modified Poisson and least-squares regression analyses for binary outcomesrqlm
Calculating stabilized weights for IPW analysis: Single time pointstabwt
Calculating stabilized weights for IPW analysis: Longitudinal datastabwtlong
Calculating stabilized weights for IPW analysis: Single time point (for more than 3 groups)stabwtmulti
Creating summary table for IPTW analysis using stabilized weightsSumStat
Pooled logistic regression for target trial emulationttemsm