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
SBdecomp_1.2.zip(r-4.5)SBdecomp_1.2.zip(r-4.4)SBdecomp_1.2.zip(r-4.3)
SBdecomp_1.2.tgz(r-4.4-any)SBdecomp_1.2.tgz(r-4.3-any)
SBdecomp_1.2.tar.gz(r-4.5-noble)SBdecomp_1.2.tar.gz(r-4.4-noble)
SBdecomp_1.2.tgz(r-4.4-emscripten)SBdecomp_1.2.tgz(r-4.3-emscripten)
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'))

Peer review:

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

Datasets:

On CRAN:

2.00 score 1 stars 150 downloads 5 exports 49 dependencies

Last updated 3 years agofrom:4d6422cb32. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:bar.sbdecompKern.FUNpred.smoothsbdecompVTM

Dependencies:clicolorspacedata.tableDBIdeldirfansifarvergbmggplot2gluegtableinterpisobandjpegjsonlitelabelinglatticelatticeExtralifecyclemagrittrMASSMatrixMatrixModelsmgcvminqamitoolsmunsellnlmenumDerivpillarpkgconfigpngR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalessurveysurvivaltibbletwangutf8vctrsviridisLitewithrxgboostxtable