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-4.7-any)SBdecomp_1.2.zip(r-4.6-any)SBdecomp_1.2.zip(r-4.5-any)
SBdecomp_1.2.tgz(r-4.6-any)SBdecomp_1.2.tgz(r-4.5-any)
SBdecomp_1.2.tar.gz(r-4.7-any)SBdecomp_1.2.tar.gz(r-4.6-any)
SBdecomp_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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 279 downloads 5 exports 41 dependencies

Last updated from:d844d9b9a4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK163
source / vignettesOK162
linux-release-x86_64OK157
macos-release-arm64OK102
macos-oldrel-arm64OK108
windows-develOK117
windows-releaseOK118
windows-oldrelOK100
wasm-releaseOK133

Exports:bar.sbdecompKern.FUNpred.smoothsbdecompVTM

Dependencies:clicpp11data.tableDBIdeldirfarvergbmggplot2gluegtableinterpisobandjpegjsonlitelabelinglatticelatticeExtralifecycleMASSMatrixMatrixModelsminqamitoolsnumDerivpngR6RColorBrewerRcppRcppArmadilloRcppEigenrlangS7scalessurveysurvivaltwangvctrsviridisLitewithrxgboostxtable