Package: WeightIt 1.3.2.9000

WeightIt: Weighting for Covariate Balance in Observational Studies

Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.

Authors:Noah Greifer [aut, cre]

WeightIt_1.3.2.9000.tar.gz
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WeightIt.pdf |WeightIt.html✨
WeightIt/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/ngreifer/weightit/issues

Datasets:
  • msmdata - Simulated data for a 3 time point sequential study

On CRAN:

causal-inferenceinverse-probability-weightsobservational-studypropensity-scores

11.36 score 102 stars 3 packages 444 scripts 5.5k downloads 4 mentions 17 exports 34 dependencies

Last updated 1 hours agofrom:c026cee438. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-winOKNov 13 2024
R-4.5-linuxOKNov 13 2024
R-4.4-winOKNov 13 2024
R-4.4-macOKNov 13 2024
R-4.3-winOKNov 13 2024
R-4.3-macOKNov 13 2024

Exports:.weightit_methodsas.weightitas.weightitMSMcalibratecoxph_weightitESSget_w_from_psglm_weightitlm_weightitmake_full_rankmultinom_weightitordinal_weightitsbpstrimweightitweightit.fitweightitMSM

Dependencies:backportschkclicobaltcolorspacecrayonfansifarvergenericsggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

Estimating Effects After Weighting

Noah Greifer

Rendered fromestimating-effects.Rmdusingknitr::rmarkdown_notangleon Nov 13 2024.

Last update: 2024-10-03
Started: 2023-04-10

Installing Supporting Packages

Noah Greifer

Rendered frominstalling-packages.Rmdusingknitr::rmarkdown_notangleon Nov 13 2024.

Last update: 2024-10-03
Started: 2022-06-22

Using WeightIt to Estimate Balancing Weights

Noah Greifer

Rendered fromWeightIt.Rmdusingknitr::rmarkdown_notangleon Nov 13 2024.

Last update: 2024-08-10
Started: 2020-08-20

Readme and manuals

Help Manual

Help pageTopics
Weighting methods.weightit_methods
Methods for 'glm_weightit()' objectsanova.glm_weightit
Create a 'weightit' object manuallyas.weightit as.weightit.default as.weightit.weightit.fit as.weightitMSM as.weightitMSM.default
Calibrate Propensity Score Weightscalibrate calibrate.default calibrate.weightit
Compute effective sample size of weighted sampleESS
Compute weights from propensity scoresget_w_from_ps
Fitting Weighted Generalized Linear Modelscoxph_weightit glm_weightit lm_weightit multinom_weightit ordinal_weightit
Methods for 'glm_weightit()' objectsglm_weightit-methods print.glm_weightit summary.coxph_weightit summary.glm_weightit summary.multinom_weightit summary.ordinal_weightit update.glm_weightit vcov.glm_weightit
Make a design matrix full rankmake_full_rank
Propensity Score Weighting Using BARTmethod_bart
Covariate Balancing Propensity Score Weightingmethod_cbps
Entropy Balancingmethod_ebal
Energy Balancingmethod_energy
Propensity Score Weighting Using Generalized Boosted Modelsmethod_gbm
Propensity Score Weighting Using Generalized Linear Modelsmethod_glm
Inverse Probability Tiltingmethod_ipt
Nonparametric Covariate Balancing Propensity Score Weightingmethod_npcbps
Optimization-Based Weightingmethod_optweight
Propensity Score Weighting Using SuperLearnermethod_super
User-Defined Functions for Estimating Weightsmethod_user
Simulated data for a 3 time point sequential studymsmdata
Plot information about the weight estimation processplot.weightit
Predictions for 'glm_weightit' objectspredict.glm_weightit predict.multinom_weightit predict.ordinal_weightit
Subgroup Balancing Propensity Scoresbps
Print and Summarize Outputplot.summary.weightit plot.summary.weightitMSM summary.weightit summary.weightitMSM
Trim (Winsorize) Large Weightstrim trim.default trim.weightit
Estimate Balancing Weightsweightit
Generate Balancing Weights with Minimal Input Processingweightit.fit
Generate Balancing Weights for Longitudinal TreatmentsweightitMSM