Package: SBdecomp 1.2

SBdecomp: Estimation of the Proportion of SB Explained by Confounders

Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi:10.1002/sim.8549>.

Authors:Layla Parast

SBdecomp_1.2.tar.gz
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SBdecomp_1.2.tgz(r-4.5-any)SBdecomp_1.2.tgz(r-4.4-any)SBdecomp_1.2.tgz(r-4.3-any)
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SBdecomp.pdf |SBdecomp.html✨
SBdecomp/json (API)

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

Bug tracker:https://github.com/laylaparast/sbdecomp/issues

Datasets:

On CRAN:

Conda:

2.00 score 1 stars 228 downloads 5 exports 49 dependencies

Last updated 3 years agofrom:4d6422cb32. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-winOKMar 05 2025
R-4.5-macOKMar 05 2025
R-4.5-linuxOKMar 05 2025
R-4.4-winOKMar 05 2025
R-4.4-macOKMar 05 2025
R-4.4-linuxOKMar 05 2025
R-4.3-winOKMar 05 2025
R-4.3-macOKMar 05 2025

Exports:bar.sbdecompKern.FUNpred.smoothsbdecompVTM

Dependencies:clicolorspacedata.tableDBIdeldirfansifarvergbmggplot2gluegtableinterpisobandjpegjsonlitelabelinglatticelatticeExtralifecyclemagrittrMASSMatrixMatrixModelsmgcvminqamitoolsmunsellnlmenumDerivpillarpkgconfigpngR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalessurveysurvivaltibbletwangutf8vctrsviridisLitewithrxgboostxtable