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 270 downloads 5 exports 41 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK168
source / vignettesOK239
linux-releaseOK151
macos-releaseOK176
macos-oldrelOK205
windows-develOK96
windows-releaseOK91
windows-oldrelOK93
wasm-releaseOK136

Exports:bar.sbdecompKern.FUNpred.smoothsbdecompVTM

Dependencies:clicpp11data.tableDBIdeldirfarvergbmggplot2gluegtableinterpisobandjpegjsonlitelabelinglatticelatticeExtralifecycleMASSMatrixMatrixModelsminqamitoolsnumDerivpngR6RColorBrewerRcppRcppArmadilloRcppEigenrlangS7scalessurveysurvivaltwangvctrsviridisLitewithrxgboostxtable