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-4.6)SBdecomp_1.2.zip(r-4.5)
SBdecomp_1.2.tgz(r-4.6-any)SBdecomp_1.2.tgz(r-4.5-any)
SBdecomp_1.2.tar.gz(r-4.6-any)SBdecomp_1.2.tar.gz(r-4.5-any)
SBdecomp_1.2.tgz(r-4.5-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'))

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

Datasets:

On CRAN:

Conda:

2.70 score 1 stars 198 downloads 5 exports 41 dependencies

Last updated from:d844d9b9a4. Checks:8 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK153
source / vignettesOK188
linux-release-x86_64OK147
macos-devel-arm64OK113
macos-release-arm64OK109
windows-develOK110
windows-releaseOK127
wasm-releaseOK109

Exports:bar.sbdecompKern.FUNpred.smoothsbdecompVTM

Dependencies:clicpp11data.tableDBIdeldirfarvergbmggplot2gluegtableinterpisobandjpegjsonlitelabelinglatticelatticeExtralifecycleMASSMatrixMatrixModelsminqamitoolsnumDerivpngR6RColorBrewerRcppRcppArmadilloRcppEigenrlangS7scalessurveysurvivaltwangvctrsviridisLitewithrxgboostxtable