Package: lmw 0.0.2

lmw: Linear Model Weights

Computes the implied weights of linear regression models for estimating average causal effects and provides diagnostics based on these weights. These diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023) <doi:10.1093/biomet/asac058> where several regression estimators are represented as weighting estimators, in connection to inverse probability weighting. 'lmw' provides tools to diagnose representativeness, balance, extrapolation, and influence for these models, clarifying the target population of inference. Tools are also available to simplify estimating treatment effects for specific target populations of interest.

Authors:Ambarish Chattopadhyay [aut], Noah Greifer [aut, cre], Jose Zubizarreta [aut]

lmw_0.0.2.tar.gz
lmw_0.0.2.zip(r-4.5)lmw_0.0.2.zip(r-4.4)lmw_0.0.2.zip(r-4.3)
lmw_0.0.2.tgz(r-4.4-any)lmw_0.0.2.tgz(r-4.3-any)
lmw_0.0.2.tar.gz(r-4.5-noble)lmw_0.0.2.tar.gz(r-4.4-noble)
lmw_0.0.2.tgz(r-4.4-emscripten)lmw_0.0.2.tgz(r-4.3-emscripten)
lmw.pdf |lmw.html
lmw/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/ngreifer/lmw/issues

Datasets:
  • lalonde - Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999).

On CRAN:

3 exports 10 stars 1.65 score 9 dependencies 16 scripts 180 downloads

Last updated 7 months agofrom:e86a8f967f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 05 2024
R-4.5-winOKSep 05 2024
R-4.5-linuxOKSep 05 2024
R-4.4-winOKSep 05 2024
R-4.4-macOKSep 05 2024
R-4.3-winOKSep 05 2024
R-4.3-macOKSep 05 2024

Exports:lmwlmw_estlmw_iv

Dependencies:backportschkcligluelatticelifecyclerlangsandwichzoo